Kernel association (KA) in statistical pattern recognition used for classification and prediction
have recently emerged in a machine learning and signal processing context. This survey …

Pattern recognition has been employed in a myriad of industrial, commercial and academic
applications. Many techniques have been devised to tackle such a diversity of applications …

Sparse representation provides an effective tool for classification under the conditions that
every class has sufficient representative training samples and the training data are …

For industrial processes, there are always some specific faults which are not easy to be
detected by the conventional PCA algorithm since the monitoring models are defined based …

Purpose: We test the hypothesis that age-related neurodegenerative eye disease can be
detected by examining patterns of eye movement recorded whilst a person naturally …

Due to the noise disturbance and limited number of training samples, within-set and
between-set sample covariance matrices in canonical correlation analysis (CCA) usually …

Dealing with high-dimensional data has always been a major problem in research of pattern
recognition and machine learning, and Linear Discriminant Analysis (LDA) is one of the …

We modify the conventional principal component analysis (PCA) and propose a novel
subspace learning framework, modified PCA (MPCA), using multiple similarity …

Open source projects leverage a large number of people to review products and improve
code quality. Differences among participants are inevitable and important to this …

Abstract Latent Low-Rank Representation (LatLRR) delivers robust and promising results for
subspace recovery and feature extraction through mining the so-called hidden effects, but …