Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Recently, contrastive learning (CL) is a promising way of learning discriminative
representations from time series data. In the representation hierarchy, semantic information …

Time series data usually contains local and global patterns. Most of the existing feature
networks focus on local features rather than the relationships among them. The latter is also …

Time series clustering is an essential unsupervised technique in cases when category
information is not available. It has been widely applied to genome data, anomaly detection …

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 …

We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …

Unsupervised feature selection chooses a subset of discriminative features to reduce feature
dimension under the unsupervised learning paradigm. Although lots of efforts have been …

Since amounts of unlabelled and high-dimensional data needed to be processed,
unsupervised feature selection has become an important and challenging problem in …

Unsupervised feature selection always occupies a key position as a preprocessing in the
tasks of classification or clustering due to the existence of extra essential features within high …