Stochastic neighbor projection on manifold for feature extraction
This paper develops a manifold-oriented stochastic neighbor projection (MSNP) technique for feature extraction. MSNP is designed to find a linear projection for the purpose of capturing the underlying pattern structure of observations that actually lie on a nonlinear manifold. In MSNP, the similarity information of observations is encoded with stochastic neighbor distribution based on geodesic distance metric, then the same distribution is required to be hold in feature space. This learning criterion not only empowers MSNP to extract nonlinear feature through a linear projection, but makes MSNP competitive as well by reason that distribution preservation is more workable and flexible than rigid distance preservation. MSNP is evaluated in three applications: data visualization for faces image, face recognition and palmprint recognition. Experimental results on several benchmark databases suggest that the proposed MSNP provides a unsupervised feature extraction approach with powerful pattern revealing capability for complex manifold data.
feature extraction visualization dimensionality reduction manifold learning biometrics