In recent years, the creation of block-structured dictionary has attracted a lot of interest. Learning such dictionaries involve two step process: block formation and dictionary update. Both these steps are important in producing an effective dictionary. The existing works mostly assume that the block structure is known a priori while learning the dictionary. For finding the unknown block struc...

We introduce an Entropy-Constrained OvercompleteBased coding scheme for natural images. The traditional overcomplete-based framework for compression is improved in its main components. The main contribution of the work is a new dictionary learning algorithm for overcomplete-based compression, referred as Entropy-Constrained Dictionary Learning. We show that the presented scheme outperforms a ba...

A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbsslice sampler, and encouraging results are reported on widely used datasets.

We explore the use of dictionary-based approaches for cross-lingual information retrieval tasks and propose a novel Coupled Dictionary Learning (CDL) algorithm to learn two separate representations simultaneously for documents in a parallel corpus alongside learning mappings from one representation to the other. We evaluate the performance of the proposed algorithm for the task of comparable do...

In many applications of compressive sensing, the dictionary providing the sparse description is partially or entirely unknown. It has been shown that dictionary learning algorithms are able to estimate the basis vectors from a set of training samples. In some applications the dictionary is multidimensional, e.g., when estimating jointly azimuth and elevation in a 2-D direction of arrival (DOA) ...

This research introduces compressed sensing (CS) principle into inverse synthetic aperture radar (ISAR) imaging of nonuniform rotation targets, and high azimuth resolution can be achieved with limited number of pulses. Firstly, the sparsity of the echoed signal of radar targets with non-uniform rotation in certain matching Fourier domain is analyzed. Then the restricted isometry property (RIP) ...

The degree of incoherence, when previsions are not made in accordance with a probability measure, is measured by the rate at which an incoherent bookie can be made a sure loser. Each bet is rescaled by one of several normalizations to account for the overall sizes of bets. For each normalization, the sure loss for incoherent previsions is divided by the normalization to determine the rate of in...

In this paper, we propose a novel dictionary learning method in the semi-supervised setting by dynamically coupling graph and group structures. To this end, samples are represented by sparse codes inheriting their graph structure while the labeled samples within the same class are represented with group sparsity, sharing the same atoms of the dictionary. Instead of statically combining graph an...

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 to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the `0 or `1-norm sparsity constraint on the representation coefficients adopted ...

There has been significant recent interest in dictionary learning and sparse coding, with applications in denoising, interpolation, feature extraction, and classification [1]–[3]. Increasingly it has been recognized that these models may be improved by imposing additional prior information, beyond sparseness. For example, a locality constraint has been used successfully in the context of featur...