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. DCMTF can be used for multi-way spectral clustering of heterogeneous collections of relational data matrices to discover latent clusters in each input matrix, across both dimensions, as well as the strengths of association across clusters. The source code for DCMTF is available on our public repository: https://bitbucket.org/cdal/dcmtf_generic
Code (1)
Tasks
ClusteringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Guided Co-training for Large-Scale Multi-View Spectral Clustering
In many real-world applications, we have access to multiple views of the data, each of which characterizes the data from a distinct aspect. Several previous algorithms have demonstrated that one can achieve better cluste…
ClusteringTensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data
In this paper, we propose a novel incomplete multi-view clustering method, in which a tensor nuclear norm regularizer elegantly diffuses the information of multi-view block-diagonal structure across different views. By e…
ClusteringIncomplete multi-view clusteringClustering Result Re-guided Incomplete Multi-view Spectral Clustering
Incomplete multi-view spectral clustering generalizes spectral clustering to multi-view data and simultaneously realizes the partition of multi-view data with missing views. For this category of method, K-means algorithm…
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike the existing methods that all adopt an off-the-shelf tensor low-rank norm without considering the special…
ClusteringSLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images
Self-supervised hyperspectral image (HSI) clustering remains a fundamental yet challenging task due to the absence of labeled data and the inherent complexity of spatial-spectral interactions. While recent advancements h…
ClusteringComputational EfficiencyContrastive LearningDenoising+1