Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

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 …

Data science models, although successful in a number of commercial domains, have had
limited applicability in scientific problems involving complex physical phenomena. Theory …

The next wave of wireless technologies will proliferate in connecting sensors, machines, and
robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT) …

In this monograph, we have attempted to summarize the actively developing field of
statistical learning with sparsity. A sparse statistical model is one having only a small …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are
in a strong need of integrative machine learning models for better use of vast volumes of …

Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

Large-scale MIMO systems are well known for their advantages in communications, but they
also have the potential for providing very accurate localization, thanks to their high angular …

For high-dimensional supervised learning problems, often using problem-specific
assumptions can lead to greater accuracy. For problems with grouped covariates, which are …