In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

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 …

Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label
data is always confronted with the curse of high-dimensional features. Feature selection has …

Neighborhood rough set based online streaming feature selection methods have aroused
wide concern in recent years and played a vital role in processing high-dimensional data …

Multi-label feature selection can efficiently handle large amounts of multi-label data.
However, two pressing issues remain in sparse learning for multi-label data. First, many …

High-dimensionality is the most noticeable characteristic of multilabel data. In practice,
multilabel data typically contain complex noises. Ignoring these noises in the feature …

As in single-label learning, multi-label learning (MLL) also suffers from the problem of “the
curse of dimensionality” due to the redundancy of features in the original data. To address …

Multi-label feature selection is a hot topic in multi-label high-dimensional data processing.
However, some multi-label feature selection models use manifold graphs. Due to its fixed …