Existing subspace clustering methods typically employ shallow models to estimate
underlying subspaces of unlabeled data points and cluster them into corresponding groups …

Intelligent data-driven fault diagnosis methods for rolling element bearings have been
widely developed in the recent years. In real industries, the collected machinery signals are …

Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …

Multi-view representation learning (MvRL) aims to learn a consensus representation from
diverse sources or domains to facilitate downstream tasks such as clustering, retrieval, and …

Multiview subspace clustering has received significant attention as the availability of diverse
of multidomain and multiview real-world data has rapidly increased in the recent years …

This paper proposes a new hashing framework to conduct similarity search via the following
steps: first, employing linear clustering methods to obtain a set of representative data points …

Abstract Linear Discriminant Analysis (LDA) is a popular technique for supervised
dimensionality reduction, and its performance is satisfying when dealing with Gaussian …

Street scene understanding is an essential task for autonomous driving. One important step
toward this direction is scene labeling, which annotates each pixel in the images with a …

A lot of works have shown that frobenius-norm-based representation (FNR) is competitive to
sparse representation and nuclear-norm-based representation (NNR) in numerous tasks …

To intelligently analyze and understand video content, a key step is to accurately perceive
the motion of the interested objects in videos. To this end, the task of object tracking, which …