In recent years, the enormous demand for computing resources resulting from massive data
and complex network models has become the limitation of deep learning. In the large-scale …

Nonoverlapping sequential pattern mining is an important type of sequential pattern mining
(SPM) with gap constraints, which not only can reveal interesting patterns to users but also …

Feature selection is important in many big data applications. Two critical challenges closely
associate with big data. First, in many big data applications, the dimensionality is extremely …

Multi-view learning has attracted increasing attention in recent years, and the existing multi-
view learning methods learn a consensus result by collecting all views. These methods have …

Multi-source streaming feature selection in an online manner has attracted considerable
attention, from researchers because it can reduce the dimensionality of heterogeneous big …

Recently, causal feature selection (CFS) has attracted considerable attention due to its
outstanding interpretability and predictability performance. Such a method primarily includes …

Nowadays the amount of networks of devices and sensors, such as smart homes or smart
cities, is rapidly increasing. Each of these devices generates massive amounts of data on a …

Tensor decomposition is a fundamental tool for multiway data analysis. While most
decomposition algorithms operate a collection of static data and perform batch processes …

Today, the dimensionality of data is increasing in a massive way. Thus, traditional feature
selection techniques are not directly applicable. Consequently, recent research has led to …

Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose
a subset of relevant samples from the input data and make reliable probabilistic predictions …