Second Order Subspace Analysis and Simple Decompositions

Abstract. The recovery of the mixture of an N-dimensional signal generated by N independent processes is a well studied problem (see e.g. [1,10]) and robust algorithms that solve this problem by Joint Diagonalization exist. While there is a lot of empirical evidence suggesting that these algorithms are also capable of solving the case where the source signals have block structure (apart from a final permutation recovery step), this claim could not be shown yet - even more, it previously was not known if this model separable at all. We present a precise definition of the subspace model, introducing the notion of simple components, show that the decomposition into simple components is unique and present an algorithm handling the decomposition task. The general task of Blind Source Separation (BSS) can be formulated as follows: Given a number of source signals (S1, . . . , SN ) = S unknown to the observer and some kind of mixture f(S1, . . . , SN ), recover the sources given only the mi...

• Type: Journal
• Year: 2010
• Where: ICA
• Authors: Harold W. Gutch, Takanori Maehara, Fabian J. Theis
• Keywords: Algorithm, ICA 2010, Signal Processing, Simple Components, final Permutation Recovery