REGULARIZED GRADIENT ALGORITHM FOR NON-NEGATIVE INDEPENDENT COMPONENT ANALYSIS

Signal Separation

Přednášející: Wendyam Serge Boris Ouedraogo, Autoři: Wendyam Serge Boris Ouedraogo, Commissariat à l'Energie Atomique et aux Energies Alternatives, France; Meriem Jaidane, Ecole Nationale d'Ingénieurs de Tunis, Tunisia; Antoine Souloumiac, Commissariat à l'Energie Atomique et aux Energies Alternatives, France; Christian Jutten, Université Joseph Fourier / Grenoble et Institut Universitaire de France, France

Independent Component Analysis (ICA) is a well-known technique for solving blind source separation (BSS) problem. However "classical" ICA algorithms seem not suited for non-negative sources. This paper proposes a gradient descent approach for solving the Non-Negative Independent Component Analysis problem (NNICA). NNICA original separation criterion contains the discontinuous sign function whose minimization may lead to ill convergence (local minima) especially for sparse sources. Replacing the discontinuous function by a continuous one tanh, we propose a more accurate regularized Gradient algorithm called "Exact" Regularized Gradient (ERG) for NNICA. Experiments on synthetic data with different sparsity degrees illustrate the efficiency of the proposedmethod and a comparison shows that the proposed ERG outperforms existing methods.

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http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5946998

Informace o přednášce

Nahráno:	2011-05-27 10:10 - 10:30, Club E
Přidáno:	20. 6. 2011 00:26
Počet zhlédnutí:	28
Rozlišení videa:	1024x576 px, 512x288 px
Délka videa:	0:18:00
Audio stopa:	MP3 [6.08 MB], 0:18:00