Tangent Distance Kernels for Support Vector Machines

by Bernaard Haasdonk, Daniel Keysers

This is a CVPR paper in 2002.

This paper doesnot explore the relationship between kernels and tangent distance(TD), which is difficult. On the contrary, they proposed two ways of incorperating both of them in a way:

• Run SVM to find sorpport vectors. Compute the TD between SVs for classification.
• Choose arbitrary RBKF, such as Gaussian, replace the Euclidian distance with TD.

Somehow, it is desirable to find a kernel representing the TD space, which is not Hilbert space, which is infeasible theoretically. Is there any other ways out?