Fault detection and diagnosis in the chemical industry is a challenging task due to the large
number of measured variables and complex interactions among them. To solve this …

Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can
effectively reflect the similarity between data samples, has been widely studied in various …

Sparse-representation-based classification (SRC) has been showing a good performance
for face recognition in recent years. But SRC is not good at face recognition with low quality …

With advantages of high concealment, non-stealing, and liveness detection,
electroencephalography (EEG) identification has a broad application prospect in the fields …

In this paper, we present a heterogeneous hashing network to generate effective and
compact hash representations of both face images and face videos for face retrieval across …

In this paper, we propose a novel viewpoint about dictionary learning (DL) and collaborative
representation for face recognition. Different from conventional learning methods, we …

We propose radar image classification via pseudo-Zernike moments based sparse
representations. We exploit invariance properties of pseudo-Zernike moments to augment …

Cross-label suppression dictionary learning is an effective approach to preserve the label
property for signal representation in face recognition. This paper presents a proposed …

Ontology matching systems are typically compared by comparing their average
performances over multiple datasets. However, this paper examines the alignment systems …

Recently, sparse representation classification (SRC) and fisher discrimination dictionary
learning (FDDL) methods have emerged as important methods for vehicle classification. In …