Abstract:
In this paper, we consider sparse representations of multidimensional signals (tensors) by generalizing the one-dimensional case (vectors). A new greedy algorithm, namely...Show MoreMetadata
Abstract:
In this paper, we consider sparse representations of multidimensional signals (tensors) by generalizing the one-dimensional case (vectors). A new greedy algorithm, namely the Tensor-OMP algorithm, is proposed to compute a block-sparse representation of a tensor with respect to a Kronecker basis where the non-zero coefficients are restricted to be located within a sub-tensor (block). It is demonstrated, through simulation examples, the advantage of considering the Kronecker structure together with the block-sparsity property obtaining faster and more precise sparse representations of tensors compared to the case of applying the classical OMP (Orthogonal Matching Pursuit).
Published in: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 25-30 March 2012
Date Added to IEEE Xplore: 30 August 2012
ISBN Information: