A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation

TL;DR

This paper introduces a parallel split-and-merge dictionary learning algorithm for sparse representation that efficiently handles large datasets by partitioning and merging, achieving comparable performance to traditional methods with reduced training time.

Contribution

The paper proposes a novel parallel dictionary learning algorithm that improves efficiency and scalability for large datasets in sparse representation tasks.

Findings

Reduces training time significantly

Maintains denoising performance comparable to standard methods

Efficient in memory and computational complexity

Abstract

In big data image/video analytics, we encounter the problem of learning an overcomplete dictionary for sparse representation from a large training dataset, which can not be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm for parallel dictionary learning. The fundamental idea behind the algorithm is to learn a sparse representation in two phases. In the first phase, the whole training dataset is partitioned into small non-overlapping subsets, and a dictionary is trained independently on each small database. In the second phase, the dictionaries are merged to form a global dictionary. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy operating on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques, that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.