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 …

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 …

As an effective method to deal with the curse of dimensionality, multi-label feature selection
aims to select the most representative subset of features by eliminating unfavorable features …

Multilabel feature selection solves the dimension distress of high-dimensional multilabel
data by selecting the optimal subset of features. Noisy and incomplete labels of raw …

In unsupervised feature selection, the clustering label matrix has the ability to distinguish
between projection clusters. However, the latent geometric structure of the clustering labels …

Feature selection plays a key role in preprocessing, effectively addressing the curse of
dimensionality in multi-label learning. While current approaches commonly utilize feature or …

Unsupervised feature selection has gained considerable attention for extracting valuable
features from unlabeled datasets. Existing approaches typically rely on sparse map** …

In recent studies, existing multilabel feature selection models have focused on either
considering the relationship between labels or the redundancy between features …

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 …

Due to the rapid growth of labels and high-dimensional data, multi-label feature selection
has attracted increasing attention. However, two common issues are ignored by existing …