Dictionary Learning Using Rank-One Atomic Decomposition (ROAD)

TL;DR

This paper introduces ROAD, a novel single-stage dictionary learning method that uses rank-one atomic decomposition, improving sparsity and data consistency, with proven convergence and superior performance on synthetic and real data.

Contribution

ROAD reformulates dictionary learning as a single-variable optimization using rank-one matrices, simplifying the process and enhancing performance over traditional two-stage methods.

Findings

ROAD outperforms benchmark algorithms on synthetic data.

ROAD performs better with small training samples.

The algorithm guarantees global convergence despite non-convexity.

Abstract

Dictionary learning aims at seeking a dictionary under which the training data can be sparsely represented. Methods in the literature typically formulate the dictionary learning problem as an optimization w.r.t. two variables, i.e., dictionary and sparse coefficients, and solve it by alternating between two stages: sparse coding and dictionary update. The key contribution of this work is a Rank-One Atomic Decomposition (ROAD) formulation where dictionary learning is cast as an optimization w.r.t. a single variable which is a set of rank one matrices. The resulting algorithm is hence single-stage. Compared with two-stage algorithms, ROAD minimizes the sparsity of the coefficients whilst keeping the data consistency constraint throughout the whole learning process. An alternating direction method of multipliers (ADMM) is derived to solve the optimization problem and the lower bound of the penalty parameter is computed to guarantees a global convergence despite non-convexity of the optimization formulation. From practical point of view, ROAD reduces the number of tuning parameters required in other benchmark algorithms. Numerical tests demonstrate that ROAD outperforms other benchmark algorithms for both synthetic data and real data, especially when the number of training samples is small.