Sparse coding plays a key role in high dimensional data analysis. One critical challenge of sparse coding is to design a dictionary that is both adaptive to the training data and generalizable to unseen data of same type. In this paper, we propose a novel dictionary learning method to build an adaptive dictionary regularized by an a-priori over-completed dictionary. This leads to a sparse struc...

Sparse coding, which is represented a vector based on sparse linear combination of a dictionary, is widely applied on signal processing, data mining and neuroscience. How to get a proper dictionary is a problem, which is data dependent and computational cost. In this paper, we treat dictionary learning in the unsupervised learning view and proposed Laplacian score dictionary (LSD). This new met...

In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that the trained models have the least degree of coherence to each other. The novelty of the prop...

—In this work we propose a new deep learning tool – deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion – one layer at a time. This requires solving a simple (shallow) dictionary learning problem; the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like s...

Title of dissertation: SPARSE DICTIONARY LEARNING AND DOMAIN ADAPTATION FOR FACE AND ACTION RECOGNITION Qiang Qiu, Doctor of Philosophy, 2013 Dissertation directed by: Professor Rama Chellappa Department of Computer Science New approaches for dictionary learning and domain adaptation are proposed for face and action recognition. We first present an approach for dictionary learning of action att...

Discriminative and Compact Dictionary Design for Hyperspectral Image Classification using Learning VQ Framework Report Title Sparse representation provides an efficient description for high-dimensional Hyperspectral Imagery (HSI) and also encodes discriminative information useful for classification. However, due to the large size of typical HSI images, the naive way to construct a dictionary wi...