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In this letter, we propose a novel computationally efficient coupleddictionary learning method that enforces pairwise correlation between the atomsof dictionaries learned to represent the underlying feature spaces of twodifferent representations of the same signals, e.g., representations indifferent modalities or representations of the same signals measured withdifferent qualities. The jointly learned correlated feature spaces representedby coupled dictionaries are used in sparse representation based classification,recognition and reconstruction tasks. The presented experimental results showthat the proposed coupled dictionary learning method has a significantly lowercomputational cost. Moreover, the visual presentation of jointly learneddictionaries shows that the pairwise correlations between the correspondingatoms are ensured.

A Fast Dictionary Learning Method for Coupled Feature Space Learning