An EM algorithm for independent component analysis using an AR-GGD source model

Authors:
Yumin Yang;Chonghui Guo;Zunquan Xia
Affiliations:
Institute of Systems Engineering, Dalian University of Technology, Dalian, P.R. China and Department of Applied Mathematics, Dalian University of Technology, Dalian, P.R. China and Department of M ...;Institute of Systems Engineering, Dalian University of Technology, Dalian, P.R. China;Department of Applied Mathematics, Dalian University of Technology, Dalian, P.R. China
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

A maximum likelihood blind source separation algorithm is developed. The temporal dependencies are explained by assuming that each source is an autoregressive (AR) process and the distribution of the associated i.i.d. innovations process is described by generalized Gaussian distribution (GGD), which can fit a broader range of statistical distributions by varying the value of the steepness parameter α. Unlike most maximum likelihood methods the proposed algorithm takes into account both spatial and temporal information. Optimization is performed using the Expectation-Maximization method, and the source model is learned alone with the demixing parameters.