Dictionary learning is a branch of signal processing and machine learning that aims at finding a frame (called dictionary) in which some training data admits a sparse representation. The sparser the representation, the better the dictionary. The resulting dictionary is in general a dense matrix, and its manipulation can be computationally costly both at the learning stage and later in the usage...

We introduce a coe cient update procedure into existing batch and online dictionary learning algorithms. We rst propose an algorithm which is a coe cient updated version of the Method of Optimal Directions (MOD) dictionary learning algorithm (DLA). The MOD algorithm with coe cient updates presents a computationally expensive dictionary learning iteration with high convergence rate. Secondly, we...

Sparsity and Nullity: Paradigm for Analysis Dictionary Learning Report Title Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and have also recently emerged with increasing research interest. Another interesting related problem based on linear equality constraint, namely the sparse null space problem (SNS), f...

Dictionary learning has played an important role in the success of sparse representation. Although synthesis dictionary learning for sparse representation has been well studied for universality representation (i.e., the dictionary is universal to all classes) and particularity representation (i.e., the dictionary is class-particular), jointly learning an analysis dictionary and a synthesis dict...

Sparse representation has long been studied and several dictionary learning methods have been proposed. The dictionary learning methods are widely used because they are adaptive. In this paper, a new dictionary learning method for audio is proposed. Signals are at first decomposed into different degrees of Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD) technique. Then t...

In this paper, it is proved that dictionary learning and sparse representation is invariant to a linear transformation. It subsumes the special case of transforming/projecting the data into a discriminative space. This is important because recently, supervised dictionary learning algorithms have been proposed, which suggest to include the category information into the learning of dictionary to ...