K-SVD Meets Transform Learning: Transform K-SVD

Ekşioğlu E. M., BAYIR O.

IEEE SIGNAL PROCESSING LETTERS, vol.21, no.3, pp.347-351, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 21 Issue: 3
  • Publication Date: 2014
  • Doi Number: 10.1109/lsp.2014.2303076
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.347-351
  • Istanbul Technical University Affiliated: Yes


Recently there has been increasing attention directed towards the analysis sparsity models. Consequently, there is a quest for learning the operators which would enable analysis sparse representations for signals in hand. Analysis operator learning algorithms such as the Analysis K-SVD have been proposed. Sparsifying transform learning is a paradigm which is similar to the analysis operator learning, but they differ in some subtle points. In this paper, we propose a novel transform operator learning algorithm called as the Transform K-SVD, which brings the transform learning and the K-SVD based analysis dictionary learning approaches together. The proposed Transform K-SVD has the important advantage that the sparse coding step of the Analysis K-SVD gets replaced with the simple thresholding step of the transform learning framework. We show that the Transform K-SVD learns operators which are similar both in appearance and performance to the operators learned from the Analysis K-SVD, while its computational complexity stays much reduced compared to the Analysis K-SVD.