Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization
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.
Cite
@article{arxiv.2109.13164,
title = {Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization},
author = {Ragunathan Mariappan and Vaibhav Rajan},
journal= {arXiv preprint arXiv:2109.13164},
year = {2021}
}