Feature selection has been an important issue in machine learning and data mining, and is
unavoidable when confronting with high‐dimensional data. With the advent of multilabel …

Clustering is an important topic in machine learning and data mining. Recently, deep
clustering, which learns feature representations for clustering tasks using deep neural …

High-dimensional multilabel data have increasingly emerged in many application areas,
suffering from two noteworthy issues: instances with high-dimensional features and large …

Abstract Structure learning based feature selection has attracted increasing attention for
selecting these features which can preserve the learned structures. However, existing …

Low-rank representation (LRR) has aroused much attention in the community of data
mining. However, it has the following twoproblems which greatly limit its applications:(1) it …

Graph-based sparse feature selection plays an important role in semi-supervised feature
selection, which greatly improves the performance of feature selection. However, most …

Multi-label learning is facing great challenges due to high-dimensional feature space,
complex label correlations and noises in multi-label data. Feature selection techniques have …

Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing
to its high efficiency and the capability to capture complementary structural information …

High-dimensional multi-label data brings challenges and difficulties in multi-label learning.
Therefore, feature selection as an effective dimension reduction technique is widely used in …

Clustering is widely used in unsupervised learning method that deals with unlabeled data.
Deep clustering has become a popular study area that relates clustering with Deep Neural …