TY  - UNPB
ID  - eprints1856
M2  - Marrakesh, Marocco
TI  - Block Orthonormal Overcomplete Dictionary Learning
AV  - public
N2  - In the field of sparse representations, the overcomplete dictionary learning problem is of crucial importance and has a growing application pool where it is used. In this paper we present an iterative dictionary learning algorithm based on the singular value decomposition that efficiently construct unions of orthonormal bases. The important innovation described in this paper, that affects positively the running time of the learning procedures, is the way in which the sparse representations are computed - data are reconstructed in a single orthonormal base, avoiding slow sparse approximation algorithms - how the bases in the union are used and updated individually and how the union itself is expanded by looking at the worst reconstructed data items. The numerical experiments show conclusively the speedup induced by our method when compared to previous works, for the same target representation
error.
KW  - Sparse representations
KW  -  orthogonal blocks
KW  -  overcomplete dictionary learning
UR  - http://eprints.imtlucca.it/1856/
N1  - This work was supported by the Romanian National Authority for Scientific Research, CNCS - UEFISCDI, project number PN-II-ID-PCE-2011-3-0400.
EP  - 5
A1  - Rusu, Cristian
A1  - Dumitrescu, Bogdan
T2  - 21st European Signal Processing Conference (EUSIPCO)
Y1  - 2013/09//
SP  - 1
ER  -