Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …

Contextual information has been shown to be powerful for semantic segmentation. This work
proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the …

Most person re-identification (re-ID) approaches are based on supervised learning, which
requires intensive manual annotation for training data. However, it is not only …

Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

Multimedia content analysis is applied in different real‐world computer vision applications,
and digital images constitute a major part of multimedia data. In last few years, the …

Despite significant progress, there remain three limitations to the previous multi-view
clustering algorithms. First, they often suffer from high computational complexity, restricting …

A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS)
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …

Parkinson's Disease (PD) is a progressive neurodegenerative disease with multiple motor
and non-motor characteristics. PD patients commonly face vocal impairments during the …

Polyadenylation [Poly (A)] is an essential process during messenger RNA (mRNA)
maturation in biological eukaryote systems. Identifying Poly (A) signals (PASs) from the …