This paper is a survey of dictionary screening for the lasso problem. The lasso problem
seeks a sparse linear combination of the columns of a dictionary to best match a given target …

In this paper, we present a novel classification model which combines the convolutional
sparse coding framework with the classification strategy. In the training phase, the proposed …

Collaborative representation-based classification has shown promising results on cognitive
vision tasks like face recognition. It solves a linear problem with l_1 l 1 or l_2 l 2 norm …

In order to address the challenge of hyperspectral image (HSI) classification with very limited
labelled samples, active learning (AL) has become a hot research issue in recent years …

Undersampled face recognition deals with the problem in which, for each subject to be
recognized, only one or few images are available in the gallery (training) set. Thus, it is very …

Conventional sparse representation based classification (SRC) represents a test sample
with the coefficient solved by each training sample in all classes. As a special version and …

Collaborative representation-based classification (CRC) is a distance based method, and it
obtains the original contributions from all samples to solve the sparse representation …

Sparse representation is a significant method to perform image classification for face
recognition. Sparsity of the image representation is the key factor for robust image …

Recently, the sparse representation of data with respect to a dictionary of features has
contributed to successful new methods in machine learning, pattern analysis, statistics and …

In this paper, we propose a novel method of human identification by ear images using
collaborative representation-based classification (CRC) with single training sample per …