Inspired by human brain and visual system, optoelectronic memristors-based neuromorphic
computing has attracted the interests of researchers to overcome the limitation of traditional …

Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

Semi-supervised classification methods are suitable tools to tackle training sets with large
amounts of unlabeled data and a small quantity of labeled data. This problem has been …

Face recognition is the process of identifying people through facial images. It has become
vital for security and surveillance applications and required everywhere including …

The aim of this paper is to collect in one place the basic background needed to understand
the discriminant analysis (DA) classifier to make the reader of all levels be able to get a …

Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has
achieved impressive results for multi-view subspace clustering, but it does not well deal with …

Making a high-dimensional (eg, 100K-dim) feature for face recognition seems not a good
idea because it will bring difficulties on consequent training, computation, and storage. This …

Binary feature descriptors such as local binary patterns (LBP) and its variations have been
widely used in many face recognition systems due to their excellent robustness and strong …

In many computer vision systems, the same object can be observed at varying viewpoints or
even by different sensors, which brings in the challenging demand for recognizing objects …

Adapting the classifier trained on a source domain to recognize instances from a new target
domain is an important problem that is receiving recent attention. In this paper, we present …