Dictionary Learning for Sparse Representation: A Novel Approach

Mostafa Sadeghi, Massoud Babaie-Zadeh, Christian Jutten
2013 IEEE Signal Processing Letters  
A dictionary learning problem is a matrix factorization in which the goal is to factorize a training data matrix, Y, as the product of a dictionary, D, and a sparse coefficient matrix, X, as follows, Y ≃ DX. Current dictionary learning algorithms minimize the representation error subject to a constraint on D (usually having unit column-norms) and sparseness of X. The resulting problem is not convex with respect to the pair (D, X). In this letter, we derive a first order series expansion formula
more » ... s expansion formula for the factorization, DX. The resulting objective function is jointly convex with respect to D and X. We simply solve the resulting problem using alternating minimization and apply some of the previously suggested algorithms onto our new problem. Simulation results on recovery of a known dictionary and dictionary learning for natural image patches show that our new problem considerably improves performance with a little additional computational load.
doi:10.1109/lsp.2013.2285218 fatcat:64e6rpvvlvfm3metk2e7qe5uyi