Multi-way Clustering and Discordance Analysis through Deep Collective
Matrix Tri-Factorization
- URL: http://arxiv.org/abs/2109.13164v1
- Date: Mon, 27 Sep 2021 16:24:23 GMT
- Title: Multi-way Clustering and Discordance Analysis through Deep Collective
Matrix Tri-Factorization
- Authors: Ragunathan Mariappan, Vaibhav Rajan
- Abstract summary: We advance the state-of-the-art in neural unsupervised learning to analyze Heterogeneous multi-typed, multimodal relational 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.
We illustrate its utility in quality assessment of knowledge bases and in improving representation learning.
- Score: 9.283501363468728
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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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