Kernel-based Hierarchical Discriminant Regression
Guo Yuefei · Jisuanji gongcheng · 2007
Hierarchical discriminant regression(HDR)casts classification problems(class labels as output)and regression problems(numeric values as output)into a unified regression problem.Clustering is performed in both output space and input space at each internal node,termed “doubly clustered”and discriminants in the input space are automatically derived from the clusters in the input space.A hierarchical probability distribution model is applied to the resulting discriminating subspace at each internal node.This realizes a coarse-to-fine approximation of probability distribution of the input samples.It is helpful in high-dimension data retrieval.Kernel method on clustering in input space is used,so the impact of the nonlinear border can be effectively reduced and the results of the retrieval will be more accurate.