DocumentCode
1396183
Title
An improved stochastic gradient algorithm for principal component analysis and subspace tracking
Author
Dehaene, Jeroen ; Moonen, Marc ; Vandewalle, Joos
Author_Institution
Belgian Nat. Fund for Sci. Res., Katholieke Univ., Leuven, Belgium
Volume
45
Issue
10
fYear
1997
fDate
10/1/1997 12:00:00 AM
Firstpage
2582
Lastpage
2586
Abstract
We propose a new stochastic gradient algorithm for principal component analysis and subspace tracking, requiring O(nm) operations per update, where n is the number of input signals, and m is the signal subspace dimension. A parallel version with problem size independent throughput is obtained at the expense of O(n2) additional flops
Keywords
computational complexity; parallel algorithms; signal flow graphs; signal processing; stochastic processes; tracking; computational complexity; input signals; parallel algorithm; principal component analysis; problem size independent throughput; signal flow graph; signal processing; signal subspace dimension; stochastic gradient algorithm; subspace tracking; Adaptive filters; Adaptive signal processing; Eigenvalues and eigenfunctions; Flow graphs; Parallel algorithms; Principal component analysis; Signal processing algorithms; Singular value decomposition; Stochastic processes; Throughput;
fLanguage
English
Journal_Title
Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1053-587X
Type
jour
DOI
10.1109/78.640724
Filename
640724
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