some papers about multi-view-clustering

very brief summaries of some papers I read

Binary Multi-View Clustering

large scale multi-view image clustering

jointly learn collaborative discrete representation and binary cluster structures

has an algorithm with proved convergence analysis

Contrastive Clustering

unified instance- and cluster-level contrastive learning

row vectors and column vectors as instance representation and cluster representation

Q: how to construct negative instance, how to design the training target

Deep Clustering: On the Link between Discriminative Models and K-Means

discover the equivilance between discriminative models using L2 regularized MI loss and soft regularized K-means loss, under some conditions

Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method

parameter-free, graph based multi-view clustering (graph fusion frameswork ?)

Dual Contrastive Prediction for Incomplete Multi-view Representation Learning

in-complete MvRL

unify consistency learning and missing data recovery: proved using information theory

framework based on contrastive learning loss

Robust Multi-view Clustering with Incomplete Information

unified framework to solve PVP and PSP

contrastive loss to eliminate the false negative samples