Semi-supervised Local Discriminant Embedding

In the paper we present an improved approach based on Semi supervised Discriminant Analysis (SDA) c tiled 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 matt ices 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 then intrinsic neighbor relations whereas the dissimilarity neighboring images no longer stick to one another Experiments have been carried out to v dictate out approach