Principal component analysis

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Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. (Wikipedia.org)






Conferences related to Principal component analysis

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2021 IEEE Photovoltaic Specialists Conference (PVSC)

Photovoltaic materials, devices, systems and related science and technology


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 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


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Periodicals related to Principal 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.


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.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


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Most published Xplore authors for Principal component analysis

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Xplore Articles related to Principal component analysis

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Round table on UHV technology in the USSR

IEEE Power Engineering Review, 1991

None


Mobil Cihazlar icin Aktivite Tanima Tabanli Adaptif Kullanici Arayuzu [Turkish-only]

2018 3rd International Conference on Computer Science and Engineering (UBMK), 2018

No English translation of this document was provided by the conference organizers.


Signal Enhancement as Minimization of Relevant Information Loss

SCC 2013; 9th International ITG Conference on Systems, Communication and Coding, 2013

We introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. We show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example we analyze principle component analysis for dimensionality reduction, discuss its optimality, and show ...


Dynamic global-principal component analysis sparse representation for distributed compressive video sampling

China Communications, 2013

Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global- Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from ...


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 ...


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Educational Resources on Principal component analysis

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IEEE-USA E-Books

  • Round table on UHV technology in the USSR

    None

  • Mobil Cihazlar icin Aktivite Tanima Tabanli Adaptif Kullanici Arayuzu [Turkish-only]

    No English translation of this document was provided by the conference organizers.

  • Signal Enhancement as Minimization of Relevant Information Loss

    We introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. We show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example we analyze principle component analysis for dimensionality reduction, discuss its optimality, and show that the relevant information loss can indeed vanish if the relevant information is concentrated on a lower-dimensional subspace of the input space.

  • Dynamic global-principal component analysis sparse representation for distributed compressive video sampling

    Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global- Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.

  • 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.



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