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 …
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 …
Scalable multi-view subspace clustering with unified anchors
Multi-view subspace clustering has received widespread attention to effectively fuse multi-
view information among multimedia applications. Considering that most existing …
view information among multimedia applications. Considering that most existing …
Multi-view clustering: A survey
Y Yang, H Wang - Big data mining and analytics, 2018 - ieeexplore.ieee.org
In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …
Multi-view clustering in latent embedding space
Previous multi-view clustering algorithms mostly partition the multi-view data in their original
feature space, the efficacy of which heavily and implicitly relies on the quality of the original …
feature space, the efficacy of which heavily and implicitly relies on the quality of the original …
Top-k Feature Selection Framework Using Robust 0–1 Integer Programming
Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …
subsequent data analysis, is a fundamental problem in machine learning and has been …
Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree …
There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity
and the quality of daily life. The treatment of these disorders is a challenging task for medical …
and the quality of daily life. The treatment of these disorders is a challenging task for medical …
Auto-weighted multi-view clustering via kernelized graph learning
Datasets are often collected from different resources or comprised of multiple
representations (ie, views). Multi-view clustering aims to analyze the multi-view data in an …
representations (ie, views). Multi-view clustering aims to analyze the multi-view data in an …
Low-rank tensor based proximity learning for multi-view clustering
Graph-oriented multi-view clustering methods have achieved impressive performances by
employing relationships and complex structures hidden in multi-view data. However, most of …
employing relationships and complex structures hidden in multi-view data. However, most of …
A feature-reduction multi-view k-means clustering algorithm
MS Yang, KP Sinaga - IEEE Access, 2019 - ieeexplore.ieee.org
The k-means clustering algorithm is the oldest and most known method in cluster analysis. It
has been widely studied with various extensions and applied in a variety of substantive …
has been widely studied with various extensions and applied in a variety of substantive …