Regularized Local Discrimimant Embedding
Yanwei Pang, Nenghai Yu · 2006
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.