Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization
This work addresses the challenge of analyzing augmented multi-view data for researchers in machine learning and data science, representing an incremental advancement by applying deep learning to an existing matrix factorization framework.
The authors tackled the problem of clustering heterogeneous relational data matrices by introducing the first deep learning architecture for collective matrix tri-factorization, enabling multi-way spectral clustering to discover latent clusters and associations across data dimensions.
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