Title :
Blind separation of uniformly distributed signals: a general approach
Author :
Basak, Jayanta ; Amari, Shun-Ichi
Author_Institution :
Machine Intelligence Unit, Indian Stat. Inst., Calcutta, India
fDate :
9/1/1999 12:00:00 AM
Abstract :
A general algorithm for blind separation of uniformly distributed signals is presented. First, maximum likelihood equations are obtained for dealing with this task. It is difficult to obtain a closed form maximum likelihood solution for arbitrary mixing matrix. The learning rules are obtained based on the geometric interpretation of the maximum likelihood estimator. The algorithm, under special constraint of orthogonal mixing matrix, is the same as the O(1/T2) convergent algorithm. Special noise correction mechanisms are incorporated in the algorithm, and it has been found that the algorithm exhibits stable performance even in the presence of large amount of noise
Keywords :
convergence of numerical methods; gradient methods; learning (artificial intelligence); maximum likelihood estimation; neural nets; signal detection; blind separation; convergence; learning rules; maximum likelihood estimation; natural gradient; neural networks; noise correction; orthogonal mixing matrix; uniformly distributed signals; Entropy; Equations; Hypercubes; Independent component analysis; Maximum likelihood estimation; Neural networks; Principal component analysis; Signal processing algorithms; Source separation; Vectors;
Journal_Title :
Neural Networks, IEEE Transactions on