Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Information Maximization Based Blind Source Separation Approach for
  Dependent and Independent Sources   We introduce a new information maximization (infomax) approach for the blind
source separation problem. The proposed framework provides an
information-theoretic perspective for determinant maximization-based structured
matrix factorization methods such as nonnegative and polytopic matrix
factorization. For this purpose, we use an alternative joint entropy measure
based on the log-determinant of covariance, which we refer to as
log-determinant (LD) entropy. The corresponding (LD) mutual information between
two vectors reflects a level of their correlation. We pose the infomax BSS
criterion as the maximization of the LD-mutual information between the input
and output of the separator under the constraint that the output vectors lie in
a presumed domain set. In contrast to the ICA infomax approach, the proposed
information maximization approach can separate both dependent and independent
sources. Furthermore, we can provide a finite sample guarantee for the perfect
separation condition in the noiseless case.

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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph