TAILIEUCHUNG - Báo cáo hóa học: " Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation | EURASIP Journal on Applied Signal Processing 2004 13 2034-2041 2004 Hindawi Publishing Corporation Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation Deniz Erdogmus Department of Computer Science and Engineering CSE Oregon Graduate Institute Oregon Health Science University Beaverton OR 97006 USA Email deniz@ Yadunandana N. Rao Computational NeuroEngineering Laboratory CNEL Department of Electrical Computer Engineering ECE University of Florida Gainesville FL 32611 USA Em ail yadu@ Hemanth Peddaneni Computational NeuroEngineering Laboratory CNEL Department of Electrical Computer Engineering ECE University of Florida Gainesville FL 32611 USA Email hemanth@ Anant Hegde Computational NeuroEngineering Laboratory CNEL Department of Electrical Computer Engineering ECE University of Florida Gainesville FL 32611 USA Email ahegde@ Jose C. Principe Computational NeuroEngineering Laboratory CNEL Department of Electrical Computer Engineering ECE University of Florida Gainesville FL 32611 USA Email principe@ Received 4 December 2003 Revised 19 March 2004 Recommended for Publication by John Sorensen Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem where most of these algorithms could be grouped into one of the following three approaches adaptation based on Hebbian updates and deflation optimization of a second-order statistical criterion like reconstruction error or output variance and fixed point update rules with deflation. In this paper we take a completely different approach that avoids deflation and the optimization of a cost function using gradients. The proposed method updates the eigenvector and eigenvalue matrices simultaneously with every new sample such that the estimates approximately track their true values as would be calculated from the current sample

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