Orthogonal local sensitive discriminant analysis algorithm based on adaptive neighborhood choosing
Zuohan Chen · Jisuanji gongcheng yu sheji · 2012
The curse of dimensionality is a problem of machine learning algorithm which is often encountered on study of high-dimensional data,while LSDA(locality sensitive discriminant analysis) solve the problem of curse of dimensionality.However,LSDA can not fully reflect the requirements that the manifold learning for neighborhood and overcome the metric distortion problem,by using the adaptive neighborhood selection method to measure the neighborhood,the Gram-Schmidt orthogonalization is introduced to get the orthogonal projection matrix.An adaptive neighborhood choosing of the orthogonal local sensitive discriminant analysis algorithm is proposed.Experimental results verify the effectiveness of the algorithm from the ORL and YALE face database.