The main problem of employing NMF to multi-view clustering is how to define the factorizations to give significant and commensurate clustering solutions.
Dec 18, 2013 · In this paper, we propose a novel NMF-based multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across ...
A matrix factorization approach for integrating multiple data views. In ECML. PKDD, pages 423–438, 2009. [14] T. Hofmann. Probabilistic latent semantic ...
Multi-view clustering based on Non-negative matrix factorization (NMF) has turned to be a very hot direction of research in the field of Pattern Reognition, ...
Weighted Multi-View Data Clustering via Joint Non-Negative Matrix ...
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A innovative NMF technique based Multiview clustering approach, which gives the more meaningful and compatible clustering solution over Numerous Views with ...
TL;DR: A innovative NMF technique based Multiview clustering approach, which gives the more meaningful and compatible clustering solution over Numerous Views ...
May 14, 2023 · In this paper, we propose a novel “one-pass” method, which integrates matrix factorization and k-means into a unified framework, named multi-view clustering.
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This method involves joint learning of coefficient matrices from factorization to acquire a unified coefficient matrix. ... ... Multi-view clustering based on ...
In this paper, we propose a Dual Auto-weighted multi-view clustering model based on Autoencoder-like NMF (DA 2 NMF), which enables a comprehensive exploration ...