Class-Dependent Locality Preserving Projections for Multimodal Scenarios

This paper proposes a method for linear feature extraction called Class-dependent Locality Preserving Projections. It is a supervised extension of the Locality Preserving Projection algorithm and it aims to work in scenarios with within-class multimodality, which are those scenarios where the scattering of the patterns follows more than one modal distribution. Differently from the classical feature extraction techniques that build their solutions based on the whole dataset, the Class-dependent Locality Preserving Projections looks at each class separately, building a specific projection for each class. The proposed technique analyses a query pattern based on the output of each class and chooses the class that better fit the pattern. The experimental study shows that the Class-dependent Locality Preserving Projections is a feature extraction technique for general purposes, however, it is particularly well succeed when applied to within-class multimodal scenarios.