Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …

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 …

Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …

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 …

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 …

This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset
of the relevant features from the original features by removing irrelevant, redundant or noisy …

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 …

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 …