Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Upper Bound of Bayesian Generalization Error in Non-negative Matrix
  Factorization   Non-negative matrix factorization (NMF) is a new knowledge discovery method
that is used for text mining, signal processing, bioinformatics, and consumer
analysis. However, its basic property as a learning machine is not yet
clarified, as it is not a regular statistical model, resulting that theoretical
optimization method of NMF has not yet established. In this paper, we study the
real log canonical threshold of NMF and give an upper bound of the
generalization error in Bayesian learning. The results show that the
generalization error of the matrix factorization can be made smaller than
regular statistical models if Bayesian learning is applied.

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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin