Title: Orthogonal matching pursuit-based incremental locally linear embedding algorithm

Authors: Yiqin Leng; Li Zhang; Jiwen Yang

Addresses: School of Computer Science and Technology, Soochow University, Suzhou 215006, China ' School of Computer Science and Technology, Soochow University, Suzhou 215006, China ' School of Computer Science and Technology, Soochow University, Suzhou 215006, China

Abstract: Locally linear embedding (LLE) is one of powerful manifold learning algorithms. However, when new data are available, it is necessary for LLE to run again with both the new data and the original data together. Thus, several incremental methods have been proposed for LLE to solve this problem. Linear incremental method is currently the most commonly used incremental approach. But the pseudo inverse solution does not possibly exist when handling incremental process. This paper deals with an incremental LLE based on orthogonal matching pursuit (ILLE-OMP), which remedies the drawback. ILLE-OMP is also a linear incremental method. Compared with other linear incremental methods, experimental results show that ILLE-OMP is promising.

Keywords: local linear embedding; LLE; sparse representation; incremental learning; orthogonal matching pursuit; OMP; manifold learning; linear incremental methods.

DOI: 10.1504/IJAACS.2015.069572

International Journal of Autonomous and Adaptive Communications Systems, 2015 Vol.8 No.2/3, pp.257 - 267

Received: 09 Jan 2013
Accepted: 25 Apr 2013

Published online: 27 May 2015 *

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