Recent high throughput experimental methods have been used to collect large biomedical
omics datasets. Clustering of single omic datasets has proven invaluable for biological and …

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 …

Feature selection (FS) aims to eliminate redundant features and choose the informative
ones. Since labeled data are not always easily available and abundant unlabeled data are …

Hyperspectral remote sensing image unsupervised classification, which assigns each pixel
of the image into a certain land-cover class without any training samples, plays an important …

High-throughput technologies have been used to generate a large amount of omics data. In
the past, single-level analysis has been extensively conducted where the omics …

Sparse learning has been proven to be a powerful techniquein supervised feature selection,
which allows toembed feature selection into the classification (or regression) problem. In …

Unsupervised feature selection always occupies a key position as a preprocessing in the
tasks of classification or clustering due to the existence of extra essential features within high …

Abstract Machine learning has been used in the past for the auxiliary diagnosis of
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …

Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label
data is always confronted with the curse of high-dimensional features. Feature selection has …

Data clustering, which is to partition the given data into different groups, has attracted much
attention. Recently various effective algorithms have been developed to tackle the task …