Physical Sciences and Mathematics Commons^™

Incorporation Of Radius-Info Can Be Simple With Simplemkl, Xinwang Liu, Lei Wang, Jianping Yin, Lingqiao Liu

Dr Lei Wang

Recent research has shown the benefit of incorporating the radius of the Minimal Enclosing Ball (MEB) of training data into Multiple Kernel Learning (MKL). However, straightforwardly incorporating this radius leads to complex learning structure and considerably increased computation. Moreover, the notorious sensitivity of this radius to outliers can adversely affect MKL. In this paper, instead of directly incorporating the radius of MEB, we incorporate its close relative, the trace of data scattering matrix, to avoid the above problems. By analyzing the characteristics of the resulting optimization, we show that the benefit of incorporating the radius of MEB can be fully …