Tensor-based algorithms for learning multidimensional separable dictionaries
Roemer F, Del Galdo G, Haardt M (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 3963-3967
Conference Proceedings Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Event location: ITA
ISBN: 9781479928927
DOI: 10.1109/ICASSP.2014.6854345
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 2-D 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 K-SVD algorithm. We propose tensor-based multidimensional extensions for both algorithms and show their improved performances numerically. © 2014 IEEE.
Involved external institutions
Germany (DE)How to cite
APA:
Roemer, F., Del Galdo, G., & Haardt, M. (2014). Tensor-based algorithms for learning multidimensional separable dictionaries. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 3963-3967). ITA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Roemer, Florian, Giovanni Del Galdo, and Martin Haardt. "Tensor-based algorithms for learning multidimensional separable dictionaries." Proceedings of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014, ITA Institute of Electrical and Electronics Engineers Inc., 2014. 3963-3967.
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