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 …

Unsupervised feature selection is one of the efficient approaches to reduce the dimension of
unlabeled high-dimensional data. We present a novel adaptive autoencoder with …

Feature selection has become one of the hot research topics in the era of big data. At the
same time, as an extension of single-valued data, interval-valued data with its inherent …

In this paper, we first propose a novel Structured Sparse Subspace Learning (S3L) module
to address the long-standing subspace sparsity issue. Elicited by proposed module, we …

Due to many unmarked data, there has been tremendous interest in develo**
unsupervised feature selection methods, among which graph-guided feature selection is …

This article provides a new strategy for the heterogeneous change detection (HCD) problem:
solving HCD from the perspective of graph signal processing (GSP). We construct a graph to …

In this article, we propose a novel feature selection approach via explicitly addressing the
long-standing subspace sparsity issue. Leveraging-norm regularization for feature selection …

Unsupervised feature selection is an efficient approach of dimensionality reduction for
alleviating the curse of dimensionality in the countless unlabeled high-dimensional data. In …

In this article, we propose a novel feature selection approach, named unsupervised feature
selection with constrained-norm (row-sparsity constrained) and optimized graph (RSOGFS) …

Embedded feature selection approach guides subsequent projection matrix (selection
matrix) learning through the acquisition of pseudolabel matrix to conduct feature selection …