Identifiability of Complete Dictionary Learning

Robust Identifiability in Sparse Dictionary Learning

Sparse coding or sparse dictionary learning methods have exposed underlying sparse structure in many kinds of natural data. Here, we generalize previous results guaranteeing when the learned dictionary and sparse codes are unique up to inherent permutation and scaling ambiguities [1]. We show that these solutions are robust to the addition of measurement noise provided the data samples are suff...

A Novel Face Detection Method Based on Over-complete Incoherent Dictionary Learning

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...

Bailliere's Nurses' Complete Medical Dictionary

Speech Enhancement using Adaptive Data-Based Dictionary Learning

In this paper, a speech enhancement method based on sparse representation of data frames has been presented. Speech enhancement is one of the most applicable areas in different signal processing fields. The objective of a speech enhancement system is improvement of either intelligibility or quality of the speech signals. This process is carried out using the speech signal processing techniques ...

Hierarchical Sparse Dictionary Learning

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...