The dictionary learning problem concerns the task of representing data as sparse linear sums drawn from a smaller collection basic building blocks. In application domains where such techniques are deployed, we frequently encounter datasets some form symmetry or invariance is present. Motivated by this observation, develop framework for dictionaries under constraint that blocks remains invariant...

Task-Driven Dictionary Learning for HyperspectralImage Classification with Structured SparsityConstraints Report Title Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction error for the signal. Howe...

the current research is based on comprehensive studies in the field of arabic dictionaries compilations as well as experiences of authors in imparting the arabic language education in iran for years. as any language learning requires a dictionary, and since researchers in the area of second language teaching always suggest using monolingual dictionaries, it seems necessary to discuss the merits...

Recently, sparse coding has been widely used in many applications ranging from image recovery to pattern recognition. The low mutual coherence of a dictionary is an important property that ensures the optimality of the sparse code generated from this dictionary. Indeed, most existing dictionary learning methods for sparse coding either implicitly or explicitly tried to learn an incoherent dicti...

Dictionary learning (DL) for sparse coding based classification has been widely researched in pattern recognition in recent years. Most of the DL approaches focused on the reconstruction performance and the discriminative capability of the learned dictionary. This paper proposes a new method for learning discriminative dictionary for sparse representation based classification, called Incoherent...

A recent work introduced the concept of deep dictionary learning. The first level is a dictionary learning stage where the inputs are the training data and the outputs are the dictionary and learned coefficients. In subsequent levels of deep dictionary learning, the learned coefficients from the previous level acts as inputs. This is an unsupervised representation learning technique. In this wo...