Deep clustering: A comprehensive survey
Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
A survey on multiview clustering
Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …
groups such that subjects in the same groups are more similar to those in other groups. With …
Generalized nonconvex low-rank tensor approximation for multi-view subspace clustering
The low-rank tensor representation (LRTR) has become an emerging research direction to
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
Pseudo-supervised deep subspace clustering
Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved
impressive performance due to the powerful representation extracted using deep neural …
impressive performance due to the powerful representation extracted using deep neural …
Multi-VAE: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering
Multi-view clustering, a long-standing and important research problem, focuses on mining
complementary information from diverse views. However, existing works often fuse multiple …
complementary information from diverse views. However, existing works often fuse multiple …
Deep 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 …
across multiple views. However, in real-world scenarios, samples of multi-view are partially …
Self-supervised discriminative feature learning for deep multi-view clustering
Multi-view clustering is an important research topic due to its capability to utilize
complementary information from multiple views. However, there are few methods to consider …
complementary information from multiple views. However, there are few methods to consider …
Deep multi-view subspace clustering with unified and discriminative learning
Deep multi-view subspace clustering has achieved promising performance compared with
other multi-view clustering. However, existing deep multi-view subspace clustering only …
other multi-view clustering. However, existing deep multi-view subspace clustering only …
[HTML][HTML] Representation learning in multi-view clustering: A literature review
Multi-view clustering (MVC) has attracted more and more attention in the recent few years by
making full use of complementary and consensus information between multiple views to …
making full use of complementary and consensus information between multiple views to …
Dealmvc: Dual contrastive calibration for multi-view clustering
Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …
contrastive clustering has attracted plenty of attention in recent years. However, we observe …