A kind of dimension reduction method for classification based on hyper surface
Qing He, Xiu-rong Zhao, Zong-Zhi Shi · 2005
Based on Jordan curve theorem, a universal classification method based on hyper surface is recently put forward. The experiments show that the new method can efficiently and accurately classify large data size up to 10/sup 7/ in three-dimensional space. However, the number of training samples needed to design a classifier grows with the dimension of the features. So a way to reduce the dimension of the features without losing any essential information is needed. We put forward a kind of simple and efficient dimension reduction method without losing any essential information to improve the performance of classification based on hyper surface for high dimension data.