Spectral clustering converts the data clustering problem to the graph cut problem. It is based
on graph theory. Due to the reliable theoretical basis and good clustering performance …

Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …

The task of multi-label feature selection (MLFS) is to reduce redundant information and
generate the optimal feature subset from the original multi-label data. A variety of MLFS …

Multi-label learning algorithms have significant challenges due to high-dimensional feature
space and noises in multi-label datasets. Feature selection methods are effective techniques …

Graph-based unsupervised feature selection algorithms have been shown to be promising
for handling unlabeled and high-dimensional data. Whereas, the vast majority of those …

In multi-label learning, high-dimensionality is the most prominent characteristic of the data.
An efficient pre-processing step, named feature selection, is required to reduce “the curse of …

Multi-label feature selection, a crucial preprocessing step for multi-label classification, has
been widely applied to data mining, artificial intelligence and other fields. However, most of …

Numerous high-dimension multi-label data are produced, leading to the imperative need to
design excellent multi-label feature selection methods. It is of paramount importance to …

An orthogonal representation of features can offer valuable insights into feature selection as
it aims to find a representative subset of features in which all features can be accurately …

Multi-view and multi-label data offer a comprehensive perspective for learning models, but
dimensionality poses a challenge for feature selection. Existing methods based on …