Name
Abstract | ||
|---|---|---|
FastICA is a fast fixed-point algorithm proposed by Hyvarinen for Independent Component Analysis (ICA). For the outstanding performance such as fast convergence and robustness, it is now one of the most popular estimate methods. The existence of spurious equilibrium in FastICA is addressed in this paper, which comes from its applications in Blind Source Separation (BSS). Two issues are involved. The first is on the object function and the second is about the fixed-point algorithm, an approximate Newton's method. Analysis shows the existence of spurious equilibrium, which is the singular point introduced during the algorithm's derivation. Experimental results show the estimates of spurious equilibria, and improvements are proposed by revising the convergence condition. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28647-9_116 | ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 |
Keywords | Field | DocType |
blind source separation,objective function,fixed point,singular point,independent component analysis | Convergence (routing),Applied mathematics,Computer science,Robustness (computer science),FastICA,Artificial intelligence,Blind signal separation,Singular point of a curve,Mathematical optimization,Pattern recognition,Object function,Independent component analysis,Spurious relationship | Conference |
Volume | ISSN | Citations |
3173 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 4 | 2 |