An overview of recent multi-view clustering
With the widespread deployment of sensors and the Internet-of-Things, multi-view data has
become more common and publicly available. Compared to traditional data that describes …
become more common and publicly available. Compared to traditional data that describes …
[HTML][HTML] Microarray cancer feature selection: Review, challenges and research directions
Microarray technology has become an emerging trend in the domain of genetic research in
which many researchers employ to study and investigate the levels of genes' expression in a …
which many researchers employ to study and investigate the levels of genes' expression in a …
Hessian-based semi-supervised feature selection using generalized uncorrelated constraint
Feature selection (FS) aims to eliminate redundant features and choose the informative
ones. Since labeled data are not always easily available and abundant unlabeled data are …
ones. Since labeled data are not always easily available and abundant unlabeled data are …
Feature selection in machine learning: A new perspective
J Cai, J Luo, S Wang, S Yang - Neurocomputing, 2018 - Elsevier
High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …
machine learning and data mining. Feature selection provides an effective way to solve this …
Multi-view clustering and semi-supervised classification with adaptive neighbours
Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance in …
oriented methods have been widely investigated and achieve promising performance in …
A survey on semi-supervised feature selection methods
Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …
which eliminates irrelevant and redundant features and improves learning performance. In …
Feature selection with multi-view data: A survey
This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …
strategies, which select and combine multi-view features effectively to accomplish …
Auto-weighted multi-view learning for image clustering and semi-supervised classification
Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance …
oriented methods have been widely investigated and achieve promising performance …
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 …
Unsupervised feature selection with structured graph optimization
Since amounts of unlabelled and high-dimensional data needed to be processed,
unsupervised feature selection has become an important and challenging problem in …
unsupervised feature selection has become an important and challenging problem in …