Exact Sparse Orthogonal Dictionary Learning

Autor:	Liu, Kai, Zhao, Yongjian, Wang, Hua
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Over the past decade, learning a dictionary from input images for sparse modeling has been one of the topics which receive most research attention in image processing and compressed sensing. Most existing dictionary learning methods consider an over-complete dictionary, such as the K-SVD method, which may result in high mutual incoherence and therefore has a negative impact in recognition. On the other side, the sparse codes are usually optimized by adding the $\ell_0$ or $\ell_1$-norm penalty, but with no strict sparsity guarantee. In this paper, we propose an orthogonal dictionary learning model which can obtain strictly sparse codes and orthogonal dictionary with global sequence convergence guarantee. We find that our method can result in better denoising results than over-complete dictionary based learning methods, and has the additional advantage of high computation efficiency. Comment: need more revisions
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.09085 Zobrazit plný text záznamu View this record from Arxiv