Incomplete multi-view clustering
15 papers with code • 1 benchmarks • 0 datasets
Most implemented papers
High-order Correlation Preserved Incomplete Multi-view Subspace Clustering
Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation.
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction
XLearning-SCU/2022-TPAMI-DCP
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CVPR 2021
In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data.
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data.
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
However, different views often have distinct incompleteness, i. e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views).
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods.
V3H: View Variation and View Heredity for Incomplete Multi-view Clustering
Inspired by the variation and the heredity in genetics, V3H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively.
Tensor-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.
Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph
Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations.
Localized Sparse Incomplete Multi-view Clustering
Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation.
A Survey on Incomplete Multi-view Clustering
However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods.
