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Secure Overcomplete Dictionary Learning for Sparse Representation

Published:2020-01-01 Issue:1 Volume:E103.D Page:50-58
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

NAKACHI Takayuki¹,BANDOH Yukihiro¹,KIYA Hitoshi²

Affiliation:

1. NTT Corporation

2. Tokyo Metropolitan University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E103.D/1/E103.D_2019MUP0009/_pdf

Reference20 articles.

1. [1] B.A. Olshausen and D.J. Field, “Emergence of simple-cell receptive-field properties by learning a sparse code for natural images,” Nature, vol.381, pp.607-609, 1996. 10.1038/381607a0

2. [3] B.K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM J. Comput., vol.24, no.2, pp.227-234, 1995. 10.1137/s0097539792240406

3. [4] K. Engan, S.O. Aase, and J.H. Husoy, “Method of optimal directions for frame design,” ICASSP1999, pp.2443-2446, 1999. 10.1109/icassp.1999.760624

4. [5] M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process., vol.54, no.11, pp.4311-4322, 2006. 10.1109/tsp.2006.881199

5. [6] S.G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionary,” IEEE Trans. Signal Process., vol.41, no.12, pp.3397-3415, 1993. 10.1109/78.258082

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