Efficient and Parallel Separable Dictionary Learning

2020/07/07 by Rusu, Cristian, Irofti, Paul
#FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2007.03800

Abstract

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising.

Related

• Separable Dictionary Learning
• A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation
• Computational Intractability of Dictionary Learning for Sparse Representation
• Exact Sparse Orthogonal Dictionary Learning
• Learning computationally efficient dictionaries and their implementation as fast transforms