Title :
EM algorithms for independent component analysis
Author_Institution :
Sloan Center for Theor. Neurobiol., California Univ., San Francisco, CA, USA
fDate :
31 Aug-2 Sep 1998
Abstract :
This paper presents a new approach to the blind source separation problem. In our approach, each source density is described by a model that is quite general and fully adaptive. Based on this model, we derive and demonstrate unsupervised learning algorithms not only for square noiseless mixing, but also for the general case where the number of sources may differ from the number of observed mixtures and the data are noisy. These algorithms use expectation-maximization to estimate the arbitrary source densities, mixing matrix and noise covariance from the input data. An approximate algorithm, based on the variational framework, is developed for cases where exact learning is intractable
Keywords :
adaptive signal detection; learning systems; maximum likelihood estimation; optimisation; probability; unsupervised learning; EM algorithms; blind source separation; expectation-maximization; independent component analysis; machine learning; maximum likelihood estimation; mixing matrix; noise covariance; square noiseless mixing; unsupervised learning; Blind source separation; Covariance matrix; Filters; Gaussian noise; Independent component analysis; Maximum likelihood estimation; Parameter estimation; Unsupervised learning;
Conference_Titel :
Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
Conference_Location :
Cambridge
Print_ISBN :
0-7803-5060-X
DOI :
10.1109/NNSP.1998.710643
