PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Blind Identification of Under-Determined Mixtures based on the Characteristic Fucntion
Pierre Comon and Myriam Rajih
Signal Processing Volume 86, Number 9, pp. 2271-2281, 2006. ISSN 0165-1684

Abstract

Linear Mixtures of independent random variables (the so-called sources) are sometimes referred to as Under-Determined Mixtures (UDM) when the number of sources exceeds the dimension of the observation space. The algorithms proposed are able to identify algebraically a UDM using the second characteristic function (c.f.) of the observations, without any need of sparsity assumption on sources. In fact, by taking high-order derivatives of the multivariate c.f. core equation, the Blind Identification problem is shown to reduce to a tensor decomposition. With only two sensors, the first algorithm only needs a SVD. With a larger number of sensors, the second algorithm executes an Alternating Least Squares (ALS) algorithm. The joint use of statistics of different orders is possible, and a LS solution can be computed. Identifiability conditions are stated in each of the two cases. Computer simulations eventually demonstrate performances in the absence of sparsity, and emphasize the interest in using jointly derivatives of different orders.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:2320
Deposited By:Pierre Comon
Deposited On:22 November 2006