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

Discriminative dictionary learning (DL) has been widely studied in various pattern
classification problems. Most of the existing DL methods aim to learn a synthesis dictionary …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

In order to improve the quality of a software system, software defect prediction aims to
automatically identify defective software modules for efficient software test. To predict …

Automatic detection of geospatial targets in cluttered scenes is a profound challenge in the
field of aerial and satellite image analysis. In this paper, we propose a novel practical …

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

Sparse representation based classification (SRC) has recently been proposed for robust
face recognition. To deal with occlusion, SRC introduces an identity matrix as an occlusion …

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 decades, it has been largely unknown to what extent multiple functional networks
spatially overlap/interact with each other and jointly realize the total cortical function. Here …

Functional connectomes (FCs) have been recently shown to be powerful in characterizing
brain conditions. However, many previous studies assumed temporal stationarity of FCs …