Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization

Ragunathan Mariappan; Vaibhav Rajan

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arXiv · 2109.13164

Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization

Ragunathan Mariappan·Vaibhav Rajan

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Abstract

Heterogeneous multi-typed, multimodal relational data is increasingly available in many domains and their exploratory analysis poses several challenges. We advance the state-of-the-art in neural unsupervised learning to analyze such data. We design the first neural method for collective matrix tri-factorization of arbitrary collections of matrices to perform spectral clustering of all constituent entities and learn cluster associations. Experiments on benchmark datasets demonstrate its efficacy over previous non-neural approaches. Leveraging signals from multi-way clustering and collective matrix completion we design a unique technique, called Discordance Analysis, to reveal information discrepancies across subsets of matrices in a collection with respect to two entities. We illustrate its utility in quality assessment of knowledge bases and in improving representation learning.

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Ragunathan Mariappan, Vaibhav Rajan. 2021-09-27. Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization. https://arxiv.org/abs/2109.13164

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