[hal-01012136] Nonnegative Compression for Semi-Nonnegative Independent Component Analysis

In many Independent Component Analysis ICA problems the mixing matrix is nonnegative while the sources are unconstrained giving rise to what we call hereafter the Semi-Nonnegative ICA SN-ICA problems Exploiting the nonnegativity property can improve the ICA result Besides in some practical applications the dimension of the observation space must be reduced However the classical dimension compression procedure such as prewhitening breaks the nonnegativity property of the compressed mixing matrix In this paper we introduce a new nonnegative compression method which guarantees the nonnegativity of the compressed mixing matrix Simulation results show its fast convergence property An illustration of Blind Source Separation BSS of Magnetic Resonance Spectroscopy MRS data confirms the validity of the proposed method



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