Learning an efficient projection to map high-dimensional data into a lower dimensional
space is a rather challenging task in the community of pattern recognition and computer …

Recent advances in large‐scale image and video analysis have empowered the potential
capabilities of visual surveillance systems. In particular, deep learning‐based approaches …

By exploiting the low-dimensional structure of high-dimensional data, sparse representation
based classifiers (SRC) has recently attracted massive attention in pattern recognition. In …

Canonical correlation analysis (CCA) is a kind of classical multivariate analysis method.
Less canonical correlation variables are used to describe the relationship between two …

Feature selection plays a crucial role in data mining and pattern recognition tasks. This
paper proposes an efficient method for robust unsupervised feature selection, called joint …

In this paper, we propose a novel graph-based semisupervised learning framework, called
joint sparse representation and embedding propagation learning (JSREPL). The idea of …

In order to enhance the performance of bearing fault diagnosis and classification, features
extraction and features dimensionality reduction have become more important. The original …

Generalized low-rank approximation of matrix (GLRAM) is a multi-linear learning method
and has been widely concerned due to its outstanding performance. It can make full use of …

The sparse matrix regression (SMR) model for the feature selection method has attracted
much attention. However, most existing models do not consider the globality and adaptively …

How to define the sparse affinity weight matrices is still an open problem in existing manifold
learning algorithm. In this paper, we propose a novel supervised learning method called …