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Communication Dans Un Congrès Année : 2014

Nonnegative Compression for Semi-Nonnegative Independent Component Analysis

Résumé

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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Dates et versions

hal-01012136 , version 1 (25-06-2014)
hal-01012136 , version 2 (29-10-2014)

Identifiants

  • HAL Id : hal-01012136 , version 2

Citer

Lu Wang, Amar Kachenoura, Laurent Albera, Ahmad Karfoul, Hua Zhong Shu, et al.. Nonnegative Compression for Semi-Nonnegative Independent Component Analysis. The eighth IEEE Sensor Array and Multi-Channel Signal Processing Workshop, Jun 2014, A Coruna, Spain. 4 p. ⟨hal-01012136v2⟩
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