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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2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

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


2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)

Science, technology and applications spanning the millimeter-waves, terahertz and infrared spectral regions


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)


2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)

Photovoltaic materials, devices, systems and related science and technology


2019 IEEE 58th Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering 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, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).


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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

[{u'author_order': 1, u'authorUrl': u'https://ieeexplore.ieee.org/author/38168588700', u'full_name': u'W.A. Mittelstadt', u'id': 38168588700}] IEEE Power Engineering Review, 1991

None


Principal Component Analysis Results for Emotions and Behavioral Tendencies

[] 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2019

Principal component analysis results for emotions and behavioral tendencies.


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

[{u'author_order': 1, u'full_name': u'Bernhard C. Geiger'}, {u'author_order': 2, u'full_name': u'Gernot Kubin'}] 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

[{u'author_order': 1, u'affiliation': u'School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37540511100', u'full_name': u'Wu Minghu', u'id': 37540511100}, {u'author_order': 2, u'affiliation': u'School of Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37292595100', u'full_name': u'Chen Rui', u'id': 37292595100}, {u'author_order': 3, u'affiliation': u'School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/38667904400', u'full_name': u'Li Ran', u'id': 38667904400}, {u'author_order': 4, u'affiliation': u'School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37856296500', u'full_name': u'Zhou Shangli', u'id': 37856296500}] 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 ...


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Educational Resources on Principal component analysis

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eLearning

No eLearning Articles are currently tagged "Principal component analysis"

IEEE-USA E-Books

  • The New Limeco Story: How One Produce Company Used Third-Party Food Safety Audit Scores to Improve Its Operation

    Food safety is a major concern of many food operations. Over the past 30 years especially, companies both large and small have focused on ensuring that the food they produce is safe as well as wholesome. Hazard Analysis Critical Control Point (HACCP) programs are designed to prevent food contaminations through chemical, biological, or physical elements and are unique in that they were designed to prevent contamination during production rather than as a system for inspecting finished products. This chapter presents New Limeco's journey to safety. New Limeco is a medium-sized firm located in Homestead, Florida, and an example of one company that has experienced great success with food safety management. While some food firms might view third-party audits as an intrusion, firms with a strong food safety culture, such as New Limeco, benefit greatly from the process of external evaluation and scoring.

  • Public‐Key Infrastructure

    This chapter presents the profiles related to public‐key infrastructure (PKI) for the Internet. The PKI manages public keys automatically through the use of public‐key certificates. It provides a basis for accommodating interoperation between PKI entities. The most important series of Internet publications for all standards specifications appear in the Internet Request for Comments (RFCs) document series. Since user authentication is so important for the PKI environment, it is appropriate to discuss the concept of digital signature at an early stage. The chapter describes the functional roles of the whole entities at all levels within the PKI. It describes the operational concepts of the PKI. An algorithm for X.509 certificate path validation is also discussed. Certificate revocation list (CRLs) are used to list unexpired certificates that have been revoked. The chapter also describes an algorithm for validating certification paths.

  • Machine Learning: Data Pre‐processing

    In prognostics and health management (PHM), data pre‐processing generally involves the following tasks: data cleansing, normalization, feature discovery, and imbalanced data management. Data cleansing is the process of detecting and correcting corrupt or inaccurate data. Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature extraction, also known as dimensionality reduction, is the transformation of high‐dimensional data into a meaningful representation of reduced dimensionality, which should have a dimensionality that corresponds to the intrinsic dimensionality of the data. Linear discriminant analysis (LDA) is commonly used as a dimensionality reduction technique in the data pre‐processing step for classification and machine learning applications. Feature selection, also called variable selection/attribute selection, is the process of selecting a subset of relevant features for use in model construction. The synthetic minority oversampling technique (SMOTE) algorithm produces artificial data based on the feature space similarities between minority data points.

  • Sound Field Analysis Using Sparse Recovery

    This chapter presents a fairly descriptive approach to the use of the sparse recovery method for sound field decomposition and focuses on the technique and its application. Plane‐wave decomposition is a powerful tool for sound field analysis, and the chapter describes the implementation and capabilities of a regularized iteratively reweighted least‐squares algorithm (IRLS) sparse‐recovery algorithm for plane‐wave decomposition. The trade‐off between increasing the resolution of the plane‐wave dictionary and having an increasingly under‐determined problem leads to consideration of sparse recovery methods. The chapter argues that the sparse‐recovery approach is advantageous because one tries to explain the sound field in terms of the fewest possible number of acoustic sources. With respect to sound field reproduction, the sparse recovery methods provide a means to identify audio objects and to upscale a sound scene to higher spherical harmonic order. Finally, the chapter explains classic plane‐wave decomposition problem.

  • Introduction

    This chapter contains sections titled: * Perception * Overview of Machine Learning Techniques * Recent Developments in Computer Animation * Chapter Summary ]]>

  • Kernel Feature Extraction in Signal Processing

    Kernel‐based feature extraction and dimensionality reduction are becoming increasingly important in advanced signal processing. This is particularly relevant in applications dealing with very high‐dimensional data. Besides changing the data representation space via kernel feature extraction, another possibility is to correct for biases in the data distributions operating on the samples. This chapter reviews the main kernel feature extraction and dimensionality reduction methods, dealing with supervised, unsupervised and semi‐supervised settings. It illustrates methods in toy examples, as well as real datasets. The chapter also analyzes the connections between Hilbert‐Schmidt independence criterion (HSIC) and classical feature extraction methods. The HSIC method measures cross‐covariance in an adequate reproducing kernel Hilbert space (RKHS) by using the entire spectrum of the cross‐covariance operator. Kernel dimensionality reduction (KDR) is a supervised feature extraction method that seeks a linear transformation of the data such that it maximizes the conditional HSIC on the labels.

  • High‐Speed Transmission Line Protection Based on Empirical Orthogonal Functions

    Nowadays one can affirm with certainty that simulations using electromagnetic transients programs (EMTP) provide a faithful replica of real power systems. Based on the characteristics and patterns of fault generated transients, this chapter introduces the proposed protection algorithms. The chapter presents a case study and evaluation of the protection scheme. It also presents the general protection scheme: the first stage consists of the formulation of the empirical orthogonal functions (EOFs), which is the basis for pattern extraction of fault generated transients affecting the protected transmission line. However, in certain applications, particularly in extra‐high voltage (EHV) and ultra‐high voltage (UHV) transmission lines, fault signals are prone to be highly contaminated with non‐power frequency transients. The proposed protection scheme uses k‐nearest neighbor (kNN) for fault classification and location,which is a very simple classification method. Readers are encouraged to evaluate more sophisticated classification methods such as support vector machines and other kernel based classifiers.

  • Quaternionic Fuzzy Neural Network for View-Invariant Color Face Image Recognition

    This chapter contains sections titled: * Introduction * Face Recognition System * Quaternion-Based View-Invariant Color Face Image Recognition * Enrollment Stage and Recognition Stage for Quaternion-Based Color Face Image Correlator * Max-Product Fuzzy Neural Network Classifier * Experimental Results * Conclusion and Future Research Directions

  • Pattern Classification

    This chapter contains sections titled:IntroductionFeature ExtractionPattern‐Classification MethodsSupport Vector MachinesUnsupervised ClusteringConclusionsExercisesAppendix: Multilayer Perceptron Training



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