Weak-label-based global and local multi-view multi-label learning with three-way clustering

Zhu, Changming; Cao, Dujuan; Guo, Shuaiping; Zhou, Ri-Gui; Wei, Lai; Dong, YiLing; Miao, Duoqian

doi:10.1007/s13042-021-01450-1

Cited by 2 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study of incomplete multi-view clustering, based on deep incomplete multiview clustering [10,11], the performance of learning is high for large-scale tasks. Deep incomplete multi-view clustering aims to recover missing data through contrastive neural network learning to improve data quality.…”

Section: Introductionmentioning

confidence: 99%

Deep multi-view graph clustering with incomplete views

Chang,

Zhu,

Miao

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Deep multi-view graph clustering has made good progress in solving large-scale problems. However, existing deep multi-view graph clustering methods suffer from the following issues: (1) How to combine data processing with multi-view clustering in deep learning, (2) How to learn the local and global information of the graph in deep learning. To this end, a novel method, called deep multi-view graph clustering with the incomplete view (DMVGC-IV), is proposed in this paper, which successfully solves the above two difficulties. Specifically, deep metric learning networks are employed on multiple views to obtain the graph structure. It approximately maintains the semantic distance of data points in the subspace. Then, DMVGC-IV combines global and local structures with a graph-fusion layer. By integrating autoencoder’s reconstruction and multi-view graph learning into a unified framework, our model can jointly optimize the cluster label assignments and embeddings suitable for graph clustering. Experiments use five commonly used multi-view data sets and compare them with five advanced multi-view clustering methods to verify the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%