University of Surrey

Test tubes in the lab Research in the ATI Dance Research

K-plane clustering algorithm for analysis dictionary learning

Zhang, Y, Wang, H, Wang, W and Sanei, S (2013) K-plane clustering algorithm for analysis dictionary learning

[img] Text
ZhangWWS_MLSP_2013.pdf - ["content_typename_Accepted version (post-print)" not defined]
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (599kB)
[img] Text (licence)
SRI_deposit_agreement.pdf
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (33kB)

Abstract

Analysis dictionary learning (ADL) aims to adapt dictionaries from training data based on an analysis sparse representation model. In a recent work, we have shown that, to obtain the analysis dictionary, one could optimise an objective function defined directly on the noisy signal, instead of on the estimated version of the clean signal as adopted in analysis K-SVD. Following this strategy, a new ADL algorithm using K-plane clustering is proposed in this paper, which is based on the observation that, the observed data are co-planer in the analysis sparse model. In other words, the columns of the observed data form multi-dimensional subspaces (hyperplanes), and the rows of the analysis dictionary are the normal vectors of the hyper-planes. The normal directions of the K-dimensional concentration hyper-planes can be estimated using the K-plane clustering algorithm, and then the rows of the analysis dictionary which are the normal vectors of the hyper-planes can be obtained. Experiments on natural image denoising demonstrate that the K-plane clustering algorithm provides comparable performance to the baseline algorithms, i.e. the analysis K-SVD and the subset pursuit based ADL. © 2013 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Zhang, YUNSPECIFIEDUNSPECIFIED
Wang, HUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Date : 2013
Identification Number : 10.1109/MLSP.2013.6661910
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 13:11
Last Modified : 31 Oct 2017 16:57
URI: http://epubs.surrey.ac.uk/id/eprint/806076

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800