Dictionary-based online kernel principal subspace analysis with double orthogonality preservation

Toshihisa Tanaka

ICASSP 2015accepted0 citations

Dictionary-based online kernel principal subspace analysis with double orthogonality preservation

Abstract

An adaptive online algorithm with a dictionary of observed signals for kernel principal subspace analysis is presented. A coefficient matrix for eigenfunctions is updated by a recursive least squares (RLS)-type algorithm and entries in the dictionary are adaptively added / removed preserving orthogonality of the eigenfunctions. It is shown that the orthogonalization can be implemented by analytically solvable (generalized) eigenvalues of 2×2 matrices, instead of the computation of the inverse squared root of matrix having the size of the dictionary. Numerical example is then illustrated to support the analysis.

BibTeX

@inproceedings{icassp2015_dictionarybasedo,
  title = {Dictionary-based online kernel principal subspace analysis with double orthogonality preservation},
  author = {Toshihisa Tanaka},
  booktitle = {ICASSP 2015},
  year = {2015}
}