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 …
Various dimension reduction techniques for high dimensional data analysis: a review
In the era of healthcare, and its related research fields, the dimensionality problem of high
dimensional data is a massive challenge as it contains a huge number of variables forming …
dimensional data is a massive challenge as it contains a huge number of variables forming …
Community preserving network embedding
Network embedding, aiming to learn the low-dimensional representations of nodes in
networks, is of paramount importance in many real applications. One basic requirement of …
networks, is of paramount importance in many real applications. One basic requirement of …
Multi-view clustering via joint nonnegative matrix factorization
Many real-world datasets are comprised of different representations or views which often
provide information complementary to each other. To integrate information from multiple …
provide information complementary to each other. To integrate information from multiple …
Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding
The application of most existing multi-view spectral clustering methods is generally limited
by the following three deficiencies. First, the requirement to post-processing, such as K …
by the following three deficiencies. First, the requirement to post-processing, such as K …
Partial multi-view clustering
Real data are often with multiple modalities or comingfrom multiple channels, while multi-
view clusteringprovides a natural formulation for generating clustersfrom such data …
view clusteringprovides a natural formulation for generating clustersfrom such data …
Multi-view clustering via multi-manifold regularized non-negative matrix factorization
Non-negative matrix factorization based multi-view clustering algorithms have shown their
competitiveness among different multi-view clustering algorithms. However, non-negative …
competitiveness among different multi-view clustering algorithms. However, non-negative …
Robust subspace clustering for multi-view data by exploiting correlation consensus
More often than not, a multimedia data described by multiple features, such as color and
shape features, can be naturally decomposed of multi-views. Since multi-views provide …
shape features, can be naturally decomposed of multi-views. Since multi-views provide …
Multi-view subspace clustering with intactness-aware similarity
Multi-view subspace clustering, which aims to partition a set of multi-source data into their
underlying groups, has recently attracted intensive attention from the communities of pattern …
underlying groups, has recently attracted intensive attention from the communities of pattern …
Multi-view clustering via nonnegative and orthogonal graph reconstruction
The goal of multi-view clustering is to partition samples into different subsets according to
their diverse features. Previous multi-view clustering methods mainly exist two forms: multi …
their diverse features. Previous multi-view clustering methods mainly exist two forms: multi …