Logo eprints

Clustering large datasets - bounds and applications with K-SVD

Rusu, Cristian Clustering large datasets - bounds and applications with K-SVD. UPB Scientific Bulletin, Series C: Electrical Engineering, 20 (10). pp. 1-10. ISSN 1454-234X (2012)

Full text not available from this repository.


This article presents a clustering method called T-mindot that is used to reduce the dimension of datasets in order to diminish the running time of the training algorithms. The T-mindot method is applied before the K-SVD algorithm in the context of sparse representations for the design of overcomplete dictionaries. Simulations that run on image data show the efficiency of the proposed method that leads to the substantial reduction of the execution time of K-SVD, while keeping the representation performance of the dictionaries designed using the original dataset.

Item Type: Article
Uncontrolled Keywords: sparse representations, clustering, KSVD. MSC2000: 94A 12.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Users 45 not found.
Date Deposited: 07 Mar 2013 13:07
Last Modified: 12 Mar 2013 14:58
URI: http://eprints.imtlucca.it/id/eprint/1525

Actions (login required)

Edit Item Edit Item