Abstract The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge
number of casualties and security problems. In order to reduce the spread of coronavirus …

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse
coding is presented. In addition to using class labels of training data, we also associate label …

The employed dictionary plays an important role in sparse representation or sparse coding
based image reconstruction and classification, while learning dictionaries from the training …

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse
coding is presented. In addition to using class labels of training data, we also associate label …

In this paper, we aim at learning compact and discriminative linear regression models.
Linear regression has been widely used in different problems. However, most of the existing …

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and
obtains promising performance in practical computer vision applications, especially for …

Existing block-diagonal representation studies mainly focuses on casting block-diagonal
regularization on training data, while only little attention is dedicated to concurrently learning …

We propose an image classification framework by leveraging the non-negative sparse
coding, low-rank and sparse matrix decomposition techniques (LR-Sc+ SPM). First, we …

Feature extraction plays a significant role in pattern recognition. Recently, many
representation-based feature extraction methods have been proposed and achieved …

This paper proposes a novel method, called robust latent subspace learning (RLSL), for
image classification. We formulate an RLSL problem as a joint optimization problem over …