Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor object classification via multilinear discriminant analysis
  network   This paper proposes a multilinear discriminant analysis network (MLDANet) for
the recognition of multidimensional objects, known as tensor objects. The
MLDANet is a variation of linear discriminant analysis network (LDANet) and
principal component analysis network (PCANet), both of which are the recently
proposed deep learning algorithms. The MLDANet consists of three parts: 1) The
encoder learned by MLDA from tensor data. 2) Features maps ob-tained from
decoder. 3) The use of binary hashing and histogram for feature pooling. A
learning algorithm for MLDANet is described. Evaluations on UCF11 database
indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA + LDA,
and MLDA in terms of classification for tensor objects.

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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg