Regularized Local Discrimimant Embedding

Recently, Chen et al. (CVPR 2005) proposed a new manifold embedding method, local discriminant embedding (LDE), which utilizes the neighbor and class relations of data to construct the embedding for classification. While having powerful classification ability, LDE suffers from small size sample problem, which leads to unstably numerical computation. To deal with this problem, we propose to a method of regularized LDE (RLDE) by imposing additional regularizing constraints on LDE. Experimental results show the effectiveness of the proposed method