Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-way Spectral Clustering of Augmented Multi-view Data through Deep
  Collective Matrix Tri-factorization   We present the first deep learning based architecture for collective matrix
tri-factorization (DCMTF) of arbitrary collections of matrices, also known as
augmented multi-view data. DCMTF can be used for multi-way spectral clustering
of heterogeneous collections of relational data matrices to discover latent
clusters in each input matrix, across both dimensions, as well as the strengths
of association across clusters. The source code for DCMTF is available on our
public repository: https://bitbucket.org/cdal/dcmtf_generic

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The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
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The categories of this paper are: eess
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int