A Kernel Selection Method Based on Gram-Schmidt Orthogonalization and HSIC
Kernel methods are an effective method for solving nonlinear and heterogeneous data,and the selection of kernel functions is an important issue in kernel methods.For different application problems,there is not enough theoretical basis for selecting the appropriate kernel,and improper kernel selection can degrade the performance of kernel methods.A kernel selection method based on Gram-Schmidt orthogonalization(GSO)and Hilbert-Schmidt independence criterion(HSIC-GSO)is proposed,which considers the irrelevant redundant information present in the kernel function selection process.Firstly,GSO is used to eliminate redundant information between kernel functions.Then,HSIC is used to measure the similarity between the kernel function and the ideal kernel.Finally,a set of kernel with strong discriminative ability and high diversity is obtained.The experimental results show that the HSIC-GSO method has good kernel generalization and improves the classification performance of MKL,verifying the effectiveness of the proposed method.
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