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A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of the sources. The Gram-Charlier expansion instead of the(More)
We propose a method of modifying a kernel function to improve the performance of a support vector machine classiier. This is based on the Riemannian geometrical structure induced by the kernel function. The idea is to enlarge the spatial resolution around the separating boundary surface by a conformal mapping such that the separability between classes is(More)
—An exponential family or mixture family of probability distributions has a natural hierarchical structure. This paper gives an " orthogonal " decomposition of such a system based on information geometry. A typical example is the decomposition of stochastic dependency among a number of random variables. In general, they have a complex structure of(More)
In this paper we develop several algorithms for non-negative matrix factorization (NMF) in applications to blind (or semi blind) source separation (BSS), when sources are generally statistically dependent under conditions that additional constraints are imposed such as nonnegativity, sparsity, smoothness, lower complexity or better predictability. We(More)
In this paper we discus a wide class of loss (cost) functions for non-negative matrix factorization (NMF) and derive several novel algorithms with improved efficiency and robustness to noise and out-liers. We review several approaches which allow us to obtain generalized forms of multiplicative NMF algorithms and unify some existing algorithms. We give also(More)