Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

In the last two decades, improvements in materials, sensors and machine learning
technologies have led to a rapid extension of electronic nose (EN) related research topics …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

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 …

Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer
biomechanists a wealth of data on healthy and pathological movement. To harness the …

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 …

While neural networks have been successfully applied to many natural language processing
tasks, they come at the cost of interpretability. In this paper, we propose a general …

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 …

This paper proposes two new methods (the Quantile Group LASSO and the Quantile Group
SCAD models) to evaluate the predictability of a large group of factors on carbon futures …

Correlation and association analyses are one of the most widely used statistical methods in
research fields, including microbiome and integrative multiomics studies. Correlation and …