Orthogonal Locality Minimizing Globality Maximizing Projections for Feature Extraction
By: Researcher II
Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”
Please contact us in case of a broken link from here
| Authors | Feiping Nie, Shiming Xiang, Yangqiu Song and Changshui Zhang |
| Journal/Conference Name | Optical Engineering (OE) |
| Paper Category | Computer Science |
| Paper Abstract | Locality preserving projections (LPP) is a recently developed linear-feature extraction algorithm that has been frequently used in the task of face recognition and other applications. However, LPP does not satisfy the shift-invariance property, which should be satisfied by a linear-feature extraction algorithm. In this paper, we analyze the reason and derive the shift-invariant LPP algorithm. Based on the analysis of the geometrical meaning of the shift-invariant LPP algorithm, we propose two algorithms to minimize the locality and maximize the globality under an orthogonal projection matrix. Experimental results on face recognition are presented to demonstrate the effectiveness of the proposed algorithms. |
| Date of publication | 2009 |
| Code Programming Language | MATLAB |
| Comment |
Copyright Researcher II 2021