Abstract
In this paper, a new nonlinear dimensionality reduction algorithm, called enhanced supervised locally linear embedding (ESLLE), is proposed. The ESLLE method attempts to make the interclass dissimilarity definitely larger than the intraclass dissimilarity in an effort to strengthen the discriminating power and generalization ability of embedded data representation. Simulation studies on artificial manifold data demonstrate that ESLLE can give better embedding results in dimensionality reduction and is more robust to noise in comparison with the original supervised LLE (SLLE). Experimental results on extended Yale face database B and CMU PIE face databases demonstrate that ESLLE obtains better performance on face recognition compared with other famous methods such as principal component analysis (PCA), locally linear embedding (LLE) as well as SLLE.
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