Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Entropy Weighted Nonnegative Matrix Factorization Algorithm for
  Feature Representation   Nonnegative matrix factorization (NMF) has been widely used to learn
low-dimensional representations of data. However, NMF pays the same attention
to all attributes of a data point, which inevitably leads to inaccurate
representation. For example, in a human-face data set, if an image contains a
hat on the head, the hat should be removed or the importance of its
corresponding attributes should be decreased during matrix factorizing. This
paper proposes a new type of NMF called entropy weighted NMF (EWNMF), which
uses an optimizable weight for each attribute of each data point to emphasize
their importance. This process is achieved by adding an entropy regularizer to
the cost function and then using the Lagrange multiplier method to solve the
problem. Experimental results with several data sets demonstrate the
feasibility and effectiveness of the proposed method. We make our code
available at https://github.com/Poisson-EM/Entropy-weighted-NMF.

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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg