Semi-supervised Local Discriminant Embedding

Abstract

In the paper, we present an improved approach based on Semi-supervised Discriminant Analysis (SDA), called semi-supervised local discriminant embedding (SLDE), for reducing the dimensionality of the feature space. We take the manifold structure into account and try to learn a subspace in which the Euclidean distances can better reflect class structure of the images. The weight matrix and the scatter matrices in SDA are improved to make efficient use of both labeled and unlabeled images. After being embedded into a low-dimensional subspace, the similar images maintain their intrinsic neighbor relations, whereas the dissimilarity neighboring images no longer stick to one another. Experiments have been carried out to validate our approach.