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 is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Abstract Graph Convolutional Networks (GCNs) are widely applied in classification tasks by
aggregating the neighborhood information of each sample to output robust node …

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 …

With efficient appearance learning models, discriminative correlation filter (DCF) has been
proven to be very successful in recent video object tracking benchmarks and competitions …

Recently, ransomware attacks have been among the major threats that target a wide range
of Internet and mobile users throughout the world, especially critical cyber physical systems …

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 …