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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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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Biometric face recognition using randomfaces

Elena Battini Sönmez; Songül Albayrak; Bülent Sankur 2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010

This paper investigates the use of the Compressive Sensing (CS) technique to the classification issue. In this context, CS is used as a means to probe the nonlinear manifold on which faces under various illumination effects reside. The scheme of randomly sampled faces (Randomfaces) with nearest neighbor classifier are compared with two classical feature extraction approaches, as Eigenfaces and Fisherfaces. ...


Real-Time Multiple Event Detection and Classification Using Moving Window PCA

Mark Rafferty; Xueqin Liu; David M. Laverty; Seán McLoone IEEE Transactions on Smart Grid, 2016

This paper proposes a method for the detection and classification of multiple events in an electrical power system in real-time, namely; islanding, high frequency events (loss of load), and low frequency events (loss of generation). This method is based on principal component analysis of frequency measurements and employs a moving window approach to combat the time-varying nature of power systems, ...


Exact Principal Geodesic Analysis for data on SO(3)

Salem Said; Nicolas Courty; Nicolas Le Bihan; Stephen J. Sangwine 2007 15th European Signal Processing Conference, 2007

PGA, or Principal Geodesic Analysis, is an extension of the classical PCA (Principal Component Analysis) to the case of data taking values on a Riemannian manifold. In this paper a new and original algorithm, for the exact computation of the PGA of data on the rotation group SO(3), is presented. Some properties of this algorithm are illustrated, with tests on ...


Subspace estimation by hierarchical neural PCA: analog/digital implementation constraints

A. Paraschiv-Ionescu; C. Jutten; G. Bouvier 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000

This paper addresses the issue of hardware implementation of hierarchical neural principal component analysis (PCA). We attempt to show by experimental studies the effect of finite accuracy computations on the algorithm's performance, for both analog and digital implementation


SAR Target Configuration Recognition Using Tensor Global and Local Discriminant Embedding

Xiayuan Huang; Hong Qiao; Bo Zhang IEEE Geoscience and Remote Sensing Letters, 2016

This letter proposes a method that can preserve the global and local discriminative information based on the tensor representation to achieve feature extraction for synthetic aperture radar (SAR) target configuration recognition. We model SAR images of targets with different configurations as different manifolds, and each manifold is represented as a collection of maximal linear patches (MLPs), each depicted by a ...


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Educational Resources on Principal component analysis

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eLearning

Biometric face recognition using randomfaces

Elena Battini Sönmez; Songül Albayrak; Bülent Sankur 2010 IEEE 18th Signal Processing and Communications Applications Conference, 2010

This paper investigates the use of the Compressive Sensing (CS) technique to the classification issue. In this context, CS is used as a means to probe the nonlinear manifold on which faces under various illumination effects reside. The scheme of randomly sampled faces (Randomfaces) with nearest neighbor classifier are compared with two classical feature extraction approaches, as Eigenfaces and Fisherfaces. ...


Real-Time Multiple Event Detection and Classification Using Moving Window PCA

Mark Rafferty; Xueqin Liu; David M. Laverty; Seán McLoone IEEE Transactions on Smart Grid, 2016

This paper proposes a method for the detection and classification of multiple events in an electrical power system in real-time, namely; islanding, high frequency events (loss of load), and low frequency events (loss of generation). This method is based on principal component analysis of frequency measurements and employs a moving window approach to combat the time-varying nature of power systems, ...


Exact Principal Geodesic Analysis for data on SO(3)

Salem Said; Nicolas Courty; Nicolas Le Bihan; Stephen J. Sangwine 2007 15th European Signal Processing Conference, 2007

PGA, or Principal Geodesic Analysis, is an extension of the classical PCA (Principal Component Analysis) to the case of data taking values on a Riemannian manifold. In this paper a new and original algorithm, for the exact computation of the PGA of data on the rotation group SO(3), is presented. Some properties of this algorithm are illustrated, with tests on ...


Subspace estimation by hierarchical neural PCA: analog/digital implementation constraints

A. Paraschiv-Ionescu; C. Jutten; G. Bouvier 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000

This paper addresses the issue of hardware implementation of hierarchical neural principal component analysis (PCA). We attempt to show by experimental studies the effect of finite accuracy computations on the algorithm's performance, for both analog and digital implementation


SAR Target Configuration Recognition Using Tensor Global and Local Discriminant Embedding

Xiayuan Huang; Hong Qiao; Bo Zhang IEEE Geoscience and Remote Sensing Letters, 2016

This letter proposes a method that can preserve the global and local discriminative information based on the tensor representation to achieve feature extraction for synthetic aperture radar (SAR) target configuration recognition. We model SAR images of targets with different configurations as different manifolds, and each manifold is represented as a collection of maximal linear patches (MLPs), each depicted by a ...


More eLearning Resources

IEEE-USA E-Books

  • Appendix C: Principal Component Analysis

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

  • 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

  • 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?

  • Online Feature Extraction for Evolving Intelligent Systems

    This chapter contains sections titled: Introduction Incremental Principal Component Analysis (IPCA) Chunk Incremental Principal Component Analysis (CIPCA) Performance Evaluation Conclusion and Future Work References

  • 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.

  • A Tutorial Introduction

    This chapter contains sections titled: Data Representation and Similarity, A Simple Pattern Recognition Algorithm, Some Insights From Statistical Learning Theory, Hyperplane Classifiers, Support Vector Classification, Support Vector Regression, Kernel Principal Component Analysis, Empirical Results and Implementations

  • Classification of Time-Embedded EEG Using Short-Time Principal Component Analysis

    Principal component analysis (PCA) is often used to project high-dimensional signals to lower dimensional subspaces defined by basis vectors that maximize the variance of the projected signals. The projected values can be used as features for classification problems. Data containing variations of relatively short duration and small magnitude, such as those seen in EEG signals, may not be captured by PCA when applied to time series of long duration. Instead, PCA can be applied independently to short segments of data and the basis vectors themselves can be used as features for classification. Here this is called the short-time principal component analysis (STPCA). In addition, the time- embedding of EEG samples is investigated prior to STPCA, resulting in a representation that captures EEG variations in space and time. The resulting features of the analysis are then classified via a standard linear discriminant analysis (LDA). Results are shown for two datasets of EEG, one recorded from subjects performing five mental tasks, and one from the third BCI Competition recorded from subjects performing one mental task and two imagined movement tasks.

  • Principal Component Analysis and Factor Analysis

    This chapter contains sections titled: Introduction, ICA and PCA, Eigenvectors and Eigenvalues, PCA Applied to Speech Signal Mixtures, Factor Analysis, Summary

  • KernelBased Clustering

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



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