Independent component analysis

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Independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals. (Wikipedia.org)






Conferences related to Independent component analysis

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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 57th ACM/ESDA/IEEE Design Automation Conference (DAC)

The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2022 59th ACM/ESDA/IEEE Design Automation Conference (DAC)

    The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2021 58th ACM/ESDA/IEEE Design Automation Conference (DAC)

    The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2019 56th ACM/ESDA/IEEE Design Automation Conference (DAC)

    EDA (Electronics Design Automation) is becoming ever more important with the continuous scaling of semiconductor devices and the growing complexities of their use in circuits and systems. Demands for lower-power, higher-reliability and more agile electronic systems raise new challenges to both design and design automation of such systems. For the past five decades, the primary focus of research track at DAC has been to showcase leading-edge research and practice in tools and methodologies for the design of circuits and systems.

  • 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)

    The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC)

    The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC)

    The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC)

    The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)

    DAC Description for TMRF The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading

  • 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC)

    The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 session on design methodologies and EDA tool developments, keynotes, panels, plus User Track presentations. A diverse worldwide community representing more than 1,000 organization attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.

  • 2012 49th ACM/EDAC/IEEE Design Automation Conference (DAC)

    The Design Automation Conference (DAC) is the premier event for the design of electronic circuits and systems, and for EDA and silicon solutions. DAC features a wide array of technical presentations plus over 200 of the leading electronics design suppliers

  • 2011 48th ACM/EDAC/IEEE Design Automation Conference (DAC)

    The Design Automation Conference is the world s leading technical conference and tradeshow on electronic design and design automation. DAC is where the IC Design and EDA ecosystem learns, networks, and does business.

  • 2010 47th ACM/EDAC/IEEE Design Automation Conference (DAC)

    The Design Automation Conference (DAC) is the premier event for the design of electronic circuits and systems, and for EDA and silicon solutions. DAC features a wide array of technical presentations plus over 200 of the leading electronics design suppliers.

  • 2009 46th ACM/EDAC/IEEE Design Automation Conference (DAC)

    DAC is the premier event for the electronic design community. DAC offers the industry s most prestigious technical conference in combination with the biggest exhibition, bringing together design, design automation and manufacturing market influencers.

  • 2008 45th ACM/EDAC/IEEE Design Automation Conference (DAC)

    The Design Automation Conference (DAC) is the premier event for the design of electronic circuits and systems, and for EDA and silicon solutions. DAC features a wide array of technical presentations plus over 250 of the leading electronics design suppliers.

  • 2007 44th ACM/IEEE Design Automation Conference (DAC)

    The Design Automation Conference (DAC) is the premier Electronic Design Automation (EDA) and silicon solution event. DAC features over 50 technical sessions covering the latest in design methodologies and EDA tool developments and an Exhibition and Demo Suite area with over 250 of the leading EDA, silicon and IP Providers.

  • 2006 43rd ACM/IEEE Design Automation Conference (DAC)

  • 2005 42nd ACM/IEEE Design Automation Conference (DAC)

  • 2004 41st ACM/IEEE Design Automation Conference (DAC)

  • 2003 40th ACM/IEEE Design Automation Conference (DAC)

  • 2002 39th ACM/IEEE Design Automation Conference (DAC)

  • 2001 38th ACM/IEEE Design Automation Conference (DAC)

  • 2000 37th ACM/IEEE Design Automation Conference (DAC)

  • 1999 36th ACM/IEEE Design Automation Conference (DAC)

  • 1998 35th ACM/IEEE Design Automation Conference (DAC)

  • 1997 34th ACM/IEEE Design Automation Conference (DAC)

  • 1996 33rd ACM/IEEE Design Automation Conference (DAC)


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE International Conference on Consumer Electronics (ICCE)

The International Conference on Consumer Electronics (ICCE) is soliciting technical papersfor oral and poster presentation at ICCE 2018. ICCE has a strong conference history coupledwith a tradition of attracting leading authors and delegates from around the world.Papers reporting new developments in all areas of consumer electronics are invited. Topics around the major theme will be the content ofspecial sessions and tutorials.


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Periodicals related to Independent component analysis

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Antennas and Wireless Propagation Letters, IEEE

IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


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Most published Xplore authors for Independent component analysis

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Xplore Articles related to Independent component analysis

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Using PCA and ICA for exploratory data analysis in situation awareness

Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001 (Cat. No.01TH8590), 2001

This paper presents an approach for analyzing hand held device usage situation (context) phenomena. The situation information under examination is multidimensional fuzzy feature information derived from multisensor measurements. The analysis is conducted using principal component analysis (PCA) and independent component analysis (ICA). PCA is used to fuse multidimensional feature information into a more compact representation while the ICA is applied ...


A common neural-network model for unsupervised exploratory data analysis and independent component analysis

IEEE Transactions on Neural Networks, 1998

This paper presents the derivation of an unsupervised learning algorithm, which enables the identification and visualization of latent structure within ensembles of high-dimensional data. This provides a linear projection of the data onto a lower dimensional subspace to identify the characteristic structure of the observations independent latent causes. The algorithm is shown to be a very promising tool for unsupervised ...


Independent component analysis for face recognition based on two dimension symmetrical image matrix

2012 24th Chinese Control and Decision Conference (CCDC), 2012

The new face recognition method based on the matrix of symmetrical face image ICA is put forward for the problem that the influence of the light on the face recognition and high dimensional small sample exists in traditional independent component analysis (ICA)in face recognition. At the same time, in order to improve the human face recognition efficiency, the ICA face ...


Performance Enhancement of Downlink Multiuser DS-CDMA Detectors Using Processing by Independent Component Analysis

2006 International Conference on Computer Engineering and Systems, 2006

In this paper, the attention is focused on the generation of the received digital signal and its linear detection over a synchronous multiuser direct sequence code division multiple access (DS-CDMA) downlink system considering various spreading codes, additive white Gaussian noise (AWGN) and introducing varying signal-to-noise ratios (SNRs) and users' power distributions. The aim of the paper is to test the ...


A Novel Feature Extraction Method and Its Relationships with PCA and KPCA

2008 Chinese Conference on Pattern Recognition, 2008

A new feature extraction method for high dimensional data using least squares support vector regression (LSSVR) is presented. Firstly, the expressions of optimal projection vectors are derived into the same form as that in the LSSVR algorithm by specially extending the feature of training samples. So the optimal projection vectors could be obtained by LSSVR. Then, using the kernel tricks, ...


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Educational Resources on Independent component analysis

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IEEE-USA E-Books

  • Using PCA and ICA for exploratory data analysis in situation awareness

    This paper presents an approach for analyzing hand held device usage situation (context) phenomena. The situation information under examination is multidimensional fuzzy feature information derived from multisensor measurements. The analysis is conducted using principal component analysis (PCA) and independent component analysis (ICA). PCA is used to fuse multidimensional feature information into a more compact representation while the ICA is applied to extract patterns containing independent low level information about the situation. The results show that a few principal components compress the situation data representation efficiently. In addition, principal component representation provides a method for visualizing high level situation information. Most independent components extracted from the usage situation data correlate strongly with some of the original signals. This suggests that the original context data already consist of relatively independent signals if the temporal relations in the data are omitted.

  • A common neural-network model for unsupervised exploratory data analysis and independent component analysis

    This paper presents the derivation of an unsupervised learning algorithm, which enables the identification and visualization of latent structure within ensembles of high-dimensional data. This provides a linear projection of the data onto a lower dimensional subspace to identify the characteristic structure of the observations independent latent causes. The algorithm is shown to be a very promising tool for unsupervised exploratory data analysis and data visualization. Experimental results confirm the attractiveness of this technique for exploratory data analysis and an empirical comparison is made with the recently proposed generative topographic mapping (GTM) and standard principal component analysis (PCA). Based on standard probability density models a generic nonlinearity is developed which allows both (1) identification and visualization of dichotomised clusters inherent in the observed data and (2) separation of sources with arbitrary distributions from mixtures, whose dimensionality may be greater than that of number of sources. The resulting algorithm is therefore also a generalized neural approach to independent component analysis (ICA) and it is considered to be a promising method for analysis of real-world data that will consist of sub- and super- Gaussian components such as biomedical signals.

  • Independent component analysis for face recognition based on two dimension symmetrical image matrix

    The new face recognition method based on the matrix of symmetrical face image ICA is put forward for the problem that the influence of the light on the face recognition and high dimensional small sample exists in traditional independent component analysis (ICA)in face recognition. At the same time, in order to improve the human face recognition efficiency, the ICA face recognition method based on the matrix of symmetrical face image is put forward. The method uses the natural characteristics with mirror symmetry of face. According to parity decomposition principle, the odd and even symmetrical samples are created. And symmetrical face image is used as training sample. The principal component analysis (PCA) is used to remove second order relevant and reduce dimension, and then the handled sample is feature extracted by ICA. According to the theory analysis and experimental proof, the influence caused by view, light, face expression, the posture change factors on the face is effective reduced by the new algorithm. Meanwhile, the algorithm increases the size of training sample and reduces the complexity of calculation. In the meantime, the algorithm solves the problem of small sample and improve face recognition rate.

  • Performance Enhancement of Downlink Multiuser DS-CDMA Detectors Using Processing by Independent Component Analysis

    In this paper, the attention is focused on the generation of the received digital signal and its linear detection over a synchronous multiuser direct sequence code division multiple access (DS-CDMA) downlink system considering various spreading codes, additive white Gaussian noise (AWGN) and introducing varying signal-to-noise ratios (SNRs) and users' power distributions. The aim of the paper is to test the efficiency of implementing an independent component analysis (ICA) based estimator of the transmitted symbols preceded by principal component analysis (PCA) pre-processing of the received data to reduce dimension and decrease the noise effects. The noisy ICA algorithm is used either as: (1) a pre-processor prior to conventional detection: The idea here is to estimate the mixing matrix that contains the basic vectors and fading terms using the separating matrix and PCA subspace or as (2) a post- processor attached to a linear DS-CDMA receiver. Numerical simulations indicate that the first schema could constitute a powerful tool for channel and symbols estimations and has the advantage that it doesn't necessitate any prior knowledge of the users' codes or paths delays and strengths except providing a short pilot sequence (presenting 1% to 4% of the transmitted symbols of the user of interest) that is used to give a good enough initial guess of the demising matrix columns and so force the ICA iteration to be in the wanted user subspace. The second schema shows performance comparable to or superior than the exact blind detectors at relatively high SNR values

  • A Novel Feature Extraction Method and Its Relationships with PCA and KPCA

    A new feature extraction method for high dimensional data using least squares support vector regression (LSSVR) is presented. Firstly, the expressions of optimal projection vectors are derived into the same form as that in the LSSVR algorithm by specially extending the feature of training samples. So the optimal projection vectors could be obtained by LSSVR. Then, using the kernel tricks, the data are mapped from the original input space to a high dimensional feature, and nonlinear feature extraction is here realized from linear version. Finally, it is proved that 1) the method presented has the same result as principal component analysis (PCA). 2) This method is more suitable for the higher dimensional input space compared. 3) The nonlinear feature extraction of the method is equivalent to kernel principal component analysis (KPCA).

  • Blind source separation of more sources than mixtures using overcomplete representations

    Empirical results were obtained for the blind source separation of more sources than mixtures using a previously proposed framework for learning overcomplete representations. This technique assumes a linear mixing model with additive noise and involves two steps: (1) learning an overcomplete representation for the observed data and (2) inferring sources given a sparse prior on the coefficients. We demonstrate that three speech signals can be separated with good fidelity given only two mixtures of the three signals. Similar results were obtained with mixtures of two speech signals and one music signal.

  • Inferring the eigenvalues of covariance matrices from limited, noisy data

    The eigenvalue spectrum of covariance matrices is of central importance to a number of data analysis techniques. Usually, the sample covariance matrix is constructed from a limited number of noisy samples. We describe a method of inferring the true eigenvalue spectrum from the sample spectrum. Results of Silverstein (1986), which characterize the eigenvalue spectrum of the noise covariance matrix, and inequalities between the eigenvalues of Hermitian matrices are used to infer probability densities for the eigenvalues of the noise-free covariance matrix, using Bayesian inference. Posterior densities for each eigenvalue are obtained, which yield error estimates. The evidence framework gives estimates of the noise variance and permits model order selection by estimating the rank of the covariance matrix. The method is illustrated with numerical examples.

  • Nonnegative Principal Component Analysis for Cancer Molecular Pattern Discovery

    As a well-established feature selection algorithm, principal component analysis (PCA) is often combined with the state-of-the-art classification algorithms to identify cancer molecular patterns in microarray data. However, the algorithm's global feature selection mechanism prevents it from effectively capturing the latent data structures in the high-dimensional data. In this study, we investigate the benefit of adding nonnegative constraints on PCA and develop a nonnegative principal component analysis algorithm (NPCA) to overcome the global nature of PCA. A novel classification algorithm NPCA-SVM is proposed for microarray data pattern discovery. We report strong classification results from the NPCA-SVM algorithm on five benchmark microarray data sets by direct comparison with other related algorithms. We have also proved mathematically and interpreted biologically that microarray data will inevitably encounter overfitting for an SVM/PCA-SVM learning machine under a Gaussian kernel. In addition, we demonstrate that nonnegative principal component analysis can be used to capture meaningful biomarkers effectively.

  • Study on fault diagnosis of blast furnace based on ICA-QNN

    Focusing on the fuzziness problem of fault classification borders, and on the diagnostic uncertainty of overlapping data, a fault diagnosis method for furnace state based on independent component analysis (ICA) and quantum neural network (QNN) was presented. Firstly, the fast ICA algorithm was applied successfully to separate the state signals of fault blast furnace and to extract their state features. Secondly, QNN was used together to accomplish the fault diagnosis of furnace state, because it possesses better functions (abilities) of pattern recognition for fault with overlapping classes and uncertainty. The experimental results demonstrate that the ICA-QNN algorithms can recognize the fault pattern of furnace state effectively and accurately. Meanwhile, it also provided a new method with fault diagnosis for blast furnace.

  • Hybrid Independent Component Analysis and Rough Set Approach for Audio Feature Extraction

    Audio classification is based on audio features. The choice of audio features can reflect important audio classification features in time and frequency time. The extraction and analysis of audio features are the base and important of audio classification. The most important problem is to extract audio features effectively and make them mutual independence to reduce information redundancy. In this paper, combined with independent component analysis and rough set, a method for audio feature extraction is presented and it's proved better performance by experiments.



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