Fast and Compact Kronecker-structured Dictionary Learning for Classification and Representation

In this paper, we present a computationally fast, storage-efficient approach, termed Kronecker-Structured Learning of Discriminative Dictionaries (K-SLD2), for learning a Kronecker-structured, overcomplete dictionary for the classification and representation of multidimensional signals like images, medical tomographic data, and videos. We evaluate the performance of K-SLD2 on several datasets, including Extended YaleB and the UCI EEG database. The use of Kronecker-structured dictionaries improves the classification performance over state-of-the-art dictionary-based methods when the number of training samples is small, at it is competitive with methods employing SIFT features even without feature extraction. Furthermore, Kronecker-structured dictionaries offer a more compact representation of signal classes, packing in more atoms with no more than 5% of the storage requirements of existing subspace models.