Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Tensorbased algorithms for learning multidimensional separable dictionaries
 Institute of Electrical and Electronics Engineers IEEE; IEEE Signal Processing Society: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014. Vol.5 : Florence, Italy, 4  9 May 2014 Piscataway, NJ: IEEE, 2014 ISBN: 9781479928941 ISBN: 9781479928927 ISBN: 9781479928934 pp.39633967 
 International Conference on Acoustics, Speech and Signal Processing (ICASSP) <39, 2014, Florence> 

 English 
 Conference Paper 
 Fraunhofer IIS () 
 compressed sensing 
Abstract
Compressive Sensing (CS) allows to acquire signals at sampling rates significantly lower than the Nyquist rate, provided that the signals possess a sparse representation in an appropriate basis. However, in some applications of CS, the dictionary providing the sparse description is partially or entirely unknown. It has been shown that dictionary learning algorithms are able to estimate the basis vectors from a set of training samples. In some applications the dictionary is multidimensional, e.g., when estimating jointly azimuth and elevation in a 2D direction of arrival (DOA) estimation context. In this paper we show that existing dictionary learning algorithms can be extended to exploit this structure, thereby providing a more accurate estimate of the dictionary. As examples we choose two prominent dictionary learning algorithms, the method of optimal directions (MOD) and the KSVD algorithm. We propose tensorbased multidimensional extensions for both algorithms and show their improved performances numerically.