TENSOR DICTIONARY LEARNINGWITH SPARSE TUCKER DECOMPOSITION

Dictionary learning algorithms are typically derived for deal-ing with one or two dimensional signals using vector-matrix operations. Little attention has been paid to the problem of dictionary learning over high dimensional tensor data. We propose a new algorithm for dictionary learning based on ten-sor factorization using a TUCKER model. In this algorithm, sparseness constraints are applied to the core tensor, of which the n-mode factors are learned from the input data in an al-ternate minimization manner using gradient descent. Simu-lations are provided to show the convergence and the recon-struction performance of the proposed algorithm. We also apply our algorithm to the speaker identification problem and compare the discriminative ability of the dictionaries learned with those of TUCKER and K-SVD algorithms. The results show that the classification performance of the dictionaries learned by our proposed algorithm is considerably better as compared to the two state of the art algorithms