An Adaptive Neighborhood Choosing of the Local Sensitive Discriminant Analysis Algorithm

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) can solve the problem of curse of dimensionality. However, LSDA can not fully reflect the requirements that the manifold learning for neighborhood, by using the adaptive neighborhood selection method to measure the neighborhood, it proposes an adaptive neighborhood choosing of the local sensitive discriminant analysis algorithm. Experimental results verify the effectiveness of the algorithm from the ORL and YALE face database.