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Outlier-resisting graph embedding
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DOI：10.1016/j.neucom.2009.08.020.png)
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Abstract
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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. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Graph embedding
Locality preserving projection
L1-norm
Outlier
Dimensionality reduction
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