Search Results for author:
Found 3 papers, 2 papers with code
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization
Heterogeneous multi-typed, multimodal relational data is increasingly available in many domains and their exploratory analysis poses several challenges.
Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization
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.
Deep Collective Matrix Factorization for Augmented Multi-View Learning
In this paper, we develop the first deep-learning based method, called dCMF, for unsupervised learning of multiple shared representations, that can model such non-linear interactions, from an arbitrary collection of matrices.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
