Open AccessAn Improved Multiplicative Updating Algorithm for Nonnegative Independent Component Analysis
Author(s) -
Li Hui,
Shen Yuehong,
Wang Jiangong
Publication year - 2013
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.13.0112.0224
Subject(s) - multiplicative function , independent component analysis , algorithm , subspace topology , gradient descent , mathematics , blind signal separation , projection (relational algebra) , minification , convergence (economics) , computer science , mathematical optimization , artificial intelligence , artificial neural network , mathematical analysis , computer network , channel (broadcasting) , economics , economic growth
This paper addresses nonnegative independent component analysis (NICA), with the aim to realize the blind separation of nonnegative well‐grounded independent source signals, which arises in many practical applications but is hardly ever explored. Recently, Bertrand and Moonen presented a multiplicative NICA (M‐NICA) algorithm using multiplicative update and subspace projection. Based on the principle of the mutual correlation minimization, we propose another novel cost function to evaluate the diagonalization level of the correlation matrix, and apply the multiplicative exponentiated gradient (EG) descent update to it to maintain nonnegativity. An efficient approach referred to as the EG‐NICA algorithm is derived and its validity is confirmed by numerous simulations conducted on different types of source signals. Results show that the separation performance of the proposed EG‐NICA algorithm is superior to that of the previous M‐NICA algorithm, with a better unmixing accuracy. In addition, its convergence speed is adjustable by an appropriate user‐defined learning rate.