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An ℓ∞ norm-Based Criterion for Bounded Component Analysis
  • Renan Brotto ,
  • Kenji Nose Filho ,
  • João Romano
Renan Brotto
UNICAMP

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Kenji Nose Filho
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João Romano
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Abstract

In the last decade, there has been a great interest in finding new priors for Blind Source Separation (BSS). Some of the most interesting of them are sparsity, non-negativity and boundedness of the sources, since they consist in weaker assumptions when compared to the classical statistical independence hypothesis. In this paper, we propose a new criterion for Bounded Component Analysis (BCA), using the $\ell_\infty$ norm, with an associated algorithm based on Givens Rotations. We analyze our proposal and evaluate it with numerical simulations for three kinds of bounded signals. From both theoretical and experimental results we show that the $\ell_\infty$ norm is a suitable contrast function for BCA, presenting very good results when compared to the state-of-the-art algorithm.