Multiview Concept Learning Via Deep Matrix Factorization

doi:10.1109/TNNLS.2020.2979532

Multiview Concept Learning Via Deep Matrix Factorization

2021-02-01

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DOI：10.1109/TNNLS.2020.2979532

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Multiview representation learning (MVRL) leverages information from multiple views to obtain a common representation summarizing the consistency and complementarity in multiview data. Most previous matrix factorization-based MVRL methods are shallow models that neglect the complex hierarchical information. The recently proposed deep multiview factorization models cannot explicitly capture consistency and complementarity in multiview data. We present the deep multiview concept learning (DMCL) method, which hierarchically factorizes the multiview data, and tries to explicitly model consistent and complementary information and capture semantic structures at the highest abstraction level. We explore two variants of the DMCL framework, DMCL-L and DMCL-N, with respectively linear/nonlinear transformations between adjacent layers. We propose two block coordinate descent-based optimization methods for DMCL-L and DMCL-N. We verify the effectiveness of DMCL on three real-world data sets for both clustering and classification tasks.

Keywords:

Sparse matrices

Encoding

Data models

Learning systems

Semantics

Optimization methods

Task analysis

Deep matrix factorization

graph embedding

multiview learning

nonnegative matrix factorization (NMF)

structured sparsity

Multiview Concept Learning Via Deep Matrix Factorization

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