A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation
This addresses the challenge of dictionary learning for big data image/video analytics, offering a more efficient approach for scenarios with storage and computational limitations, though it is incremental as it builds on existing dictionary learning methods.
The paper tackles the problem of learning an overcomplete dictionary for sparse representation from large datasets that cannot be processed at once due to storage and computational constraints, by proposing a split-and-merge algorithm that partitions the data, trains dictionaries independently on subsets, and merges them, resulting in a remarkable reduction in training time without significantly affecting denoising performance.
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.