as soon as is submitted to ZB.
Second order subspace analysis and simple decompositions.
In: Proceedings (Latent variable analysis and signal separation : 9th international conference, 27-30 September 2010, St. Malo, France). Berlin: Springer, 2010. 370-377 ( ; 6365)
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.
Edit extra informations Login
Publication type Article: Conference contribution
Keywords Statistical machine learning; Signal processing
Conference Title Latent variable analysis and signal separation : 9th international conference
Conference Date 27-30 September 2010
Conference Location St. Malo, France
Proceedings Title Proceedings
Quellenangaben Volume: 6365, Pages: 370-377
Publishing Place Berlin