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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2014 IEEE-IAS/PCA Cement Industry Technical Conference

North America's premier conference for the cement industry, attracting management and technical personnel from cement manufacturing plants and corporate offices from North, Central and South America, Europe and Asia. Includes three days of technical meetings and presentations, three afternoons of exhibits, four evenings of hospitality suites and a plant tour.

  • 2013 IEEE-IAS/PCA Cement Industry Technical Conference

    The 2013 IEEE-IAS/PCA Cement Industry Technical Conference being held at the Walt Disney World Dolphin Resort in Orlando is the leading technical conference for the cement industry. Please join us for tutorials and technical papers on a variety of topics related to the manufacturing of Portland cements.

  • 2012 IEEE-IAS/PCA Cement Industry Technical Conference

    cement Industry technical conference and Exhibits


2013 45th Southeastern Symposium on System Theory (SSST)

SSST invites papers generally focused on the subject of system theory, including topics such as control, modeling, differential and difference equations, computational methods and intelligence, neural systems, and applications of system theory.

  • 2012 Southeastern Symposium on System Theory (SSST)

    Presentation and publication of original papers from all areas of system theory, mathematical modeling design, application, and experiments/field trials. The SSST encourages well-written, high-quality papers by graduate students based on Master s thesis and Ph.D. dissertation results, as well as papers by university faculty, researchers and government/industry personnel from throughout the US and abroad.

  • 2011 IEEE 43rd Southeastern Symposium on System Theory (SSST 2011)

    system theory, mathematical modeling, design, application, and practice of system design

  • 2010 42nd Southeastern Symposium on System Theory (SSST 2010)

    Papers presented at the SSST have focused on system-issues from fields such as: mathematical theory of systems and signals, control of electro-mechanical systems, communications, signal processing, navigation, guidance, robotics, energy and power, electronic devices, computers and networks, optics, aerospace, chemical processing, manufacturing, etc.

  • 2009 41st Southeastern Symposium on System Theory (SSST 2009)

    The conference consists of invited and contributed technical papers on theoretical and applied issues associated with the architecture, modeling, simulation and analysis, control, operation and performance-evaluation of systems. This year s special focus will be on flight systems and ground and flight testing.

  • 2008 40th Southeastern Symposium on System Theory (SSST 2008)

    Theoretical and applied issues associated with the organization, modeling, simulation and analysis, control, operation and performance evaluation of systems. Focused on system issues from fields such as: mathematical theory of systems and signals, control of electro-mechanical systems, communications, signal processing, navigation, guidance, robotics, energy and power, electronic devices, computers and networks, optics, aerospace, and others.

  • 2007 39th Southeastern Symposium on System Theory (SSST 2007)

    Topics in system theory, design, application, and experimentation. These may include control theory, communications systems, signal and image processing, sensors and instrumentation, electromagnetics, radar, and power systems.


2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas)

Neural Networks

  • 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane)

    The annual IJCNN is the premier international conference in the field of neural networks.

  • 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose)

    IJCNN 2011 will include paper presentations, tutorials, workships, panels, special sessions and competitions on topics related to neural networks, including: Neural network theory and models; neural network applications; computational neuroscience; neurocognitive models; neuroengineering; neuroinformatics; neuroevolution; collective intelligence; embodied robotics; artificial life, etc.

  • 2010 International Joint Conference on Neural Networks (IJCNN 2010 - Barcelona)

  • 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta)

    IJCNN is the premier international conference in the area of neural networks theory, analysis and applications. It is organized by the International Neural Networks Society (INNS) and sponsored jointly by INNS and the IEEE Computational Intelligence Society. This is an exemplary collaboration between the two leading societies on neural networks and it provides a solid foundation for the future extensive development of the field.

  • 2008 International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong)

    The IJCNN is the premier event in the field of neural networks. It covers all topics in neural network research (broadly defined).


2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Energy and Environment, Mechatronics, Power Electronics, Signal Processing, Network and Communication Technologies.


2012 Chinese Control Conference (CCC)

The Chinese Control Conference (CCC) is an annual international conference organized by the Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA). It provides a forum for scientists and engineers over the world to present their new theoretical results and techniques in the field of systems and control. The conference consists of pre-conference workshops, plenary talks, panel discussions, invited sessions, oral sessions and poster sessions etc. for academic exchanges.

  • 2011 30th Chinese Control Conference (CCC)

    Systems and Control

  • 2010 29th Chinese Control Conference (CCC)

    S1 System Theory and Control Theory S2 Nonlinear Systems and Control S3 Complexity and Complex System Theory S4 Distributed Parameter Systems S5 Stability and Stabilization S6 Large Scale Systems S7 Stochastic Systems S8 System Modeling and System Identification S9 DEDS and Hybrid Systems S10 Optimal Control S11 Optimization and Scheduling S12 Robust Control S13 Adaptive Control and Learning Control S14 Variable Structure Control S15 Neural

  • 2008 Chinese Control Conference (CCC)

    The Chinese Control Conference (CCC) is an annual international conference organized by Tech. Com. on Control Theory, CAA. It provides a forum for scientists and engineers over the world to present their new theoretical results and techniques in the field of systems and control. The conference consists of plenary talks, panel discussions, oral and poster sessions etc. for academic exchanges. The conference proceedings have been selected for coverage in ISI proceedings/ISTP (Index to Scientific and Technical


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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.


Geoscience and Remote Sensing, IEEE Transactions on

Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.


Image Processing, IEEE Transactions on

Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.


Medical Imaging, IEEE Transactions on

Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.


Pattern Analysis and Machine Intelligence, IEEE Transactions on

Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...



Most published Xplore authors for Principal component analysis

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Xplore Articles related to Principal component analysis

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Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery

Diogo S. de Lucena; Oliver Stoller; Justin B. Rowe; Vicky Chan; David J. Reinkensmeyer 2017 International Conference on Rehabilitation Robotics (ICORR), 2017

Wearable sensing is a new tool for quantifying upper extremity (UE) rehabilitation after stroke. However, it is unclear whether it provides information beyond what is available through standard clinical assessments. To investigate this question, people with a chronic stroke (n=9) wore accelerometers on both wrists for 9 hours on a single day during their daily activities. We used principal components ...


Vehicle classification via 3D geometries

William McDowell; Lockheed Martin; Wasfy B Mikhael 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), 2016

We present a generalized mobile technique which allows for the classification of vehicles by tracking two vehicle based Points of Interest (PoI). Tracking the two PoI allows for the composition of those points into a 3D geometry, which is unique to a given vehicle type. Using high fidelity physics based simulation we demonstrate the capability to classify the 3D geometries ...


Rate-Distortion Optimized Image Compression Using Generalized Principal Component Analysis

Dohyun Ahn; Chang-Su Kim; Sang-Uk Lee 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006

A novel image compression algorithm based on generalized principal component analysis (GPCA) is proposed in this work. Each image block is first classified into a subspace and is represented with a linear combination of the basis vectors for the subspace. Therefore, the encoded information consists of subspace indices, basis vectors and transform coefficients. We adopt a vector quantization scheme and ...


Performance of MPEG-7 edge histogram descriptor in face recognition using Principal Component Analysis

Shafin Rahman; Sheikh Motahar Naim; Abdullah Al Farooq; Md. Monirul Islam 2010 13th International Conference on Computer and Information Technology (ICCIT), 2010

Face recognition is considered as a high dimensionality problem. To handle high dimensionality, a numerous methods have been proposed in literature. In this paper, we propose a novel face recognition method that efficiently solves that problem using MPEG-7 edge histogram descriptor. To the authors' knowledge, this is the first attempt to use edge histogram descriptor in face recognition. Although MPEG-7 ...


Feature Selection for Morphological Feature Extraction using Random Forests

Sveinn R. Joelsson; Jon Atli Benediktsson; Johannes R. Sveinsson Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006, 2006

Morphological feature extraction (MFE) has been successfully used to increase classification accuracy and reduce the noise level for classification of aerial images. In this paper we explore feature selection and extraction for MFE using random forests (RFs) for classification and feature selection. The approach is compared to MFE from principal components extracted from the data, by principal component analysis (PCA), ...


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Educational Resources on Principal component analysis

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eLearning

Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery

Diogo S. de Lucena; Oliver Stoller; Justin B. Rowe; Vicky Chan; David J. Reinkensmeyer 2017 International Conference on Rehabilitation Robotics (ICORR), 2017

Wearable sensing is a new tool for quantifying upper extremity (UE) rehabilitation after stroke. However, it is unclear whether it provides information beyond what is available through standard clinical assessments. To investigate this question, people with a chronic stroke (n=9) wore accelerometers on both wrists for 9 hours on a single day during their daily activities. We used principal components ...


Vehicle classification via 3D geometries

William McDowell; Lockheed Martin; Wasfy B Mikhael 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), 2016

We present a generalized mobile technique which allows for the classification of vehicles by tracking two vehicle based Points of Interest (PoI). Tracking the two PoI allows for the composition of those points into a 3D geometry, which is unique to a given vehicle type. Using high fidelity physics based simulation we demonstrate the capability to classify the 3D geometries ...


Rate-Distortion Optimized Image Compression Using Generalized Principal Component Analysis

Dohyun Ahn; Chang-Su Kim; Sang-Uk Lee 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006

A novel image compression algorithm based on generalized principal component analysis (GPCA) is proposed in this work. Each image block is first classified into a subspace and is represented with a linear combination of the basis vectors for the subspace. Therefore, the encoded information consists of subspace indices, basis vectors and transform coefficients. We adopt a vector quantization scheme and ...


Performance of MPEG-7 edge histogram descriptor in face recognition using Principal Component Analysis

Shafin Rahman; Sheikh Motahar Naim; Abdullah Al Farooq; Md. Monirul Islam 2010 13th International Conference on Computer and Information Technology (ICCIT), 2010

Face recognition is considered as a high dimensionality problem. To handle high dimensionality, a numerous methods have been proposed in literature. In this paper, we propose a novel face recognition method that efficiently solves that problem using MPEG-7 edge histogram descriptor. To the authors' knowledge, this is the first attempt to use edge histogram descriptor in face recognition. Although MPEG-7 ...


Feature Selection for Morphological Feature Extraction using Random Forests

Sveinn R. Joelsson; Jon Atli Benediktsson; Johannes R. Sveinsson Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006, 2006

Morphological feature extraction (MFE) has been successfully used to increase classification accuracy and reduce the noise level for classification of aerial images. In this paper we explore feature selection and extraction for MFE using random forests (RFs) for classification and feature selection. The approach is compared to MFE from principal components extracted from the data, by principal component analysis (PCA), ...


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IEEE-USA E-Books

  • Principal Component Analysis for Preprocessing Data

    Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions.In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method.An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA.Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code.

  • KernelBased Clustering

    This chapter contains sections titled: Introduction Kernel Principal Component Analysis Squared-Error-Based Clustering with Kernel Functions Support Vector Clustering Applications Summary

  • Data Representation Using Mixtures of Principal Components

    This chapter presents a new approach to data representation, the mixture of principal components. In general, it can be considered as a portion of a spectrum of representations and has been successfully used in such diverse applications as image compression and nonlinear clustering. At one extreme of the spectrum is vector quantization (VQ). Data are represented by a set of zero-dimensional points, Voronoi centers, within the _N_-dimensional space. Only data corresponding to the exact values of the centers are represented exactly. As such, it is a nonlinear representation. Euclidean distance is used to measure similarity under this representation. At the other extreme lies principal component analysis (PCA). Here, data are represented by a linear combination of a set of _N_ basis vectors. The representation is complete and continuous because all possible data vectors may be represented exactly. Between these two extremes lies the mixture of principal components (MPC). Data are represented by a set of _M_-dimensional subspaces where 0 < _M_ < _N_. The subspace projection length is used as the similarity measure which reduces to the vector angle for the one-dimensional case. In this approach a data vector within a class (subspace) is represented as a continuous, linear combination of the _M_ basis vectors of the subspace in a manner analogous to the PCA representation. But, because of the partitioning of the data into a discrete number of regions or classes, the MPC effects a nonlinear mapping of the data as does VQ. Applications presented include grayscale image feature extraction and color segmentation.

  • In-Network PCA and Anomaly Detection

    We consider the problem of network anomaly detection in large distributed systems. In this setting, Principal Component Analysis (PCA) has been proposed as a method for discovering anomalies by continuously tracking the projection of the data onto a residual subspace. This method was shown to work well empirically in highly aggregated networks, that is, those with a limited number of large nodes and at coarse time scales. This approach, however, has scalability limitations. To overcome these limitations, we develop a PCA-based anomaly detector in which adaptive local data filters send to a coordinator just enough data to enable accurate global detection. Our method is based on a stochastic matrix perturbation analysis that characterizes the tradeoff between the accuracy of anomaly detection and the amount of data communicated over the network.

  • Unsupervised Decomposition Methods for Analysis of Multimodal Neural Data

    Technical advances in the field of noninvasive neuroimaging allow for innovative therapeutical strategies with application potential in neural rehabilitation. To improve these methods, combinations of multiple imaging modalities have become an important topic of research. This chapter reviews some of the most popular unsupervised statistical learning techniques used in the context of neuroscientific data analysis, and places a special focus on multimodal neural data. It starts with the well-known principal component analysis (PCA). First, the chapter shows how to derive the algorithm and provides illustrative examples of the advantages and disadvantages of standard PCA. The second method presented is canonical correlation analysis (CCA): a multivariate analysis method that reveals maximally correlated features of simultaneously acquired multiple data streams. Finally the chapter presents a straightforward extension of CCA that estimates the correct solution even in the presence of noninstantaneous couplings, that is, temporal delays or convolutions between data sources.

  • Appendix C: Principal Component Analysis

    This chapter contains sections titled: Computation of the Transformation Matrix Interpretation of the Transformation Matrix

  • Overview of Independent Component Analysis

    This chapter contains sections titled: Introduction, Independent Component Analysis: What Is It?, How Independent Component Analysis Works, Independent Component Analysis and Perception, Principal Component Analysis and Factor Analysis, Independent Component Analysis: What Is It Good For?

  • Feature Extraction

    This chapter contains sections titled: Fourier Descriptor and Moment Invariants Shape Number and Hierarchical Features Corner Detection Hough Transform Principal Component Analysis Linear Discriminate Analysis Feature Reduction in Input and Feature Spaces References

  • Data Reduction

    This chapter contains sections titled: Dimensions of Large Data Sets Features Reduction Entropy Measure for Ranking Features Principal Component Analysis Values Reduction Feature Discretization: Chimerge Technique Cases Reduction Review Questions and Problems References for Further Study

  • Introduction

    This introduction provides an overview of the key concepts discussed in the subsequent chapters of this book. The book deals with advances in forecasting technologies in electric power system applications. In addition to power system load forecasting, it discusses electricity price forecasting and a storm???caused outage duration forecasting. The book offers sophisticated treatments of load forecasting, while deals with innovative approaches to price forecasting. It describes five methods: autoregressive moving average (ARMA) modeling; periodic AR modeling; exponential smoothing for double seasonality; a recently proposed alternative exponential smoothing formulation; and a method based on principal component analysis (PCA) of the daily load profiles. The book summarizes a set of stochastic process models that can be used for electricity prices according to the purpose of modeling. It also describes commonly used continuous time stochastic models such as Brownian motion, mean reversion process, geometric Brownian motion, geometric mean reversion process.



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