Sparse representation and learning has been widely used in computational intelligence,
machine learning, computer vision and pattern recognition, etc. Mathematically, solving …

Over the past decade, sparse representation, modeling, and learning has emerged and is
widely used in many visual tasks such as feature extraction and learning, object detection …

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 …

Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for
the design of better signal recovery algorithms. This information about structure is often …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

In recent years, the theories of Compressive Sensing (CS), Sparse Representation (SR) and
Dictionary Learning (DL) have emerged as powerful tools for efficiently processing data in …

In recent years, sparse representation has been widely used in object recognition
applications. How to learn the dictionary is a key issue to sparse representation. A popular …

In this paper, application of sparse representation (factorization) of signals over an
overcomplete basis (dictionary) for signal classification is discussed. Searching for the …

Modeling data is the way we-scientists-believe that information should be explained and
handled. Indeed, models play a central role in practically every task in signal and image …

Sparse signal representation, analysis, and sensing have received a lot of attention in recent
years from the signal processing, optimization, and learning communities. On one hand …