Efficient Feature Scaling for Support Vector Machines with a Quadratic Kernel
Choosing multiple hyperparameters for support vector machines has gained wide attention from researchers and this also provides a strategy for automatically selecting scaling factors of features. This paper proposes an efficient feature scaling method for support vector machines with a quadratic kernel. The proposed method alternately performs the standard SVM algorithm and the eigen-decomposition until some criteria are met. It is interesting to note that scaling factors of features can be analytically obtained for fixed support vectors. The experiments on a toy example, UCI data sets, and face images are carried out to demonstrate the feasibility and effectiveness of the proposed method.
feature scaling support vector machines minmax problem multiple parameters data classification