L1-Norm Driven Semi-supervised Local Discriminant Projection for Robust Image Representation

In this paper, we propose a L1-Norm driven Semi-Supervised Local Discriminant Projection (S2LDP-L1) for robust dimensionality reduction and image representation. For feature learning, our S2LDP-L1 approach aims at compacting local within-class divergence and separating local between-class divergence at the same time in addition to possessing the locality preserving power over all training data. To enable the presented S2LDP-L1 method to be robust against noise in data for feature reduction and representation, the L1-norm that is proven to be robust to noise and outliers is regularized on the constructed scatter matrices for measuring pairwise similarities/ dissimilarities between samples. Thus, the presentation power can be effectively enhanced to improve the subsequent classification task. The derived ratio based model is finally solved by an iterative approach to deliver a discriminating and neighborhood preserving orthogonal projection for extracting features from both training and test samples by embedding data onto it. For classification, an existing label propagation model is used to identify the categories of test data. Extensive results on handwriting digit datasets verified the validity of our S2LDP-L1, compared with other state-of-the-arts.