Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408685 | Neurocomputing | 2010 | 7 Pages |
Abstract
Graph embedding is a general framework for subspace learning. However, because of the well-known outlier-sensitiveness disadvantage of the L2-norm, conventional graph embedding is not robust to outliers which occur in many practical applications. In this paper, an improved graph embedding algorithm (termed LPP-L1) is proposed by replacing L2-norm with L1-norm. In addition to its robustness property, LPP-L1 avoids small sample size problem. Experimental results on both synthetic and real-world data demonstrate these advantages.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yanwei Pang, Yuan Yuan,