Title :
A batch subspace ICA algorithm
Author :
Mansour, Ali ; Ohnishi, Noboru
Author_Institution :
Bio-Mimetic Control Res. Center, RIKEN, Nagoya, Japan
Abstract :
For the blind source separation (BSS) problem (or independent component analysis (ICA)), it has been shown that in many situations adaptive subspace algorithms are very slow and need important computation efforts. In a previous publication, we proposed a modified subspace algorithm for stationary signals. But that algorithm was limited to stationary signals and its convergence was not fast enough. Here, we propose a batch subspace algorithm. The experimental study proves that this algorithm is very fast but its performance is not enough to achieve the complete separation of the independent components of the signals. On the other hand, this algorithm can be used as a preprocessing algorithm to initialize other adaptive subspace algorithms
Keywords :
FIR filters; adaptive signal processing; conjugate gradient methods; convergence of numerical methods; filtering theory; least mean squares methods; statistical analysis; BSS problem; FIR filters; LMS methods; adaptive subspace algorithms; batch subspace ICA algorithm; blind source separation problem; conjugate gradient algorithm; convergence; finite impulse response filters; independent component analysis; independent signal components; preprocessing algorithm; stationary signals; Adaptive control; Convergence; Finite impulse response filter; Independent component analysis; Lagrangian functions; Nonlinear filters; Programmable control; Signal processing algorithms; Statistics; World Wide Web;
Conference_Titel :
Statistical Signal and Array Processing, 2000. Proceedings of the Tenth IEEE Workshop on
Conference_Location :
Pocono Manor, PA
Print_ISBN :
0-7803-5988-7
DOI :
10.1109/SSAP.2000.870082