Doubly Aligned Incomplete Multi-view Clustering
Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of $L_{2,1}$-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringIncomplete multi-view clusteringregressionSimilar Papers 제목 키워드 기반
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. …
ClusteringIncomplete multi-view clusteringregressionScalable Incomplete Multi-View Clustering with Structure Alignment
The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or sensor malfunction, which raises the re…
Clusteringgraph constructionGraph LearningIncomplete multi-view clusteringDeep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or …
ClusteringContrastive LearningIncomplete multi-view clusteringIncomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion
Incomplete multi-view data, where certain views are entirely missing for some samples, poses significant challenges for traditional multi-view clustering methods. Existing deep incomplete multi-view clustering approaches…
Incomplete multi-view clusteringUnbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.e., balanced incompleteness. However, di…
ClusteringIncomplete multi-view clusteringMulti-view Subspace Clustering