One-Class Classification (OCC) is a special case of multi-class classification, where data
observed during training is from a single positive class. The goal of OCC is to learn a …

Conventional visual perception technology is subject to many restrictions, such as
illumination, background clutter, and occlusion. Many intrinsic properties of objects, like …

Subspace clustering refers to the problem of grouping data points that lie in a union of low-
dimensional subspaces. One successful approach for solving this problem is sparse …

The camera provides rich visual information regarding objects and becomes one of the most
mainstream sensors in the automation community. However, it is often difficult to be …

We propose a generalized Sparse Representation-based Classification (SRC) algorithm for
open set recognition where not all classes presented during testing are known during …

Despite the fact that different objects possess distinct class-specific features, they also
usually share common patterns. This observation has been exploited partially in a recently …

When there exist not enough historical defect data for building an accurate prediction model,
semisupervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are …

Dexterous robots have emerged in the last decade in response to the need for fine-motor-
control assistance in applications as diverse as surgery, undersea welding, and mechanical …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

This paper introduces a new nonlinear dictionary learning method for histograms in the
probability simplex. The method leverages optimal transport theory, in the sense that our aim …