Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation.

ABSTRACT: In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it co

SUBMITTER: Grossi G 

PROVIDER: S-EPMC5245881 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature