Multi-kernel learning method based on neural tangent kernel sketch
To address large-scale and unbalanced distribution problems,a neural tangent kernel sketch-based multiple kernel learning method(NS-MKL)was proposed.The neural tangent kernel was applied instead of the single-layer kernel function as the base kernel function of the multiple kernel learning to enhance the representation capability of the multiple kernel learning method.The neural tangent kernel was approximated using the neural tangent kernel sketch algorithm,which reduced the number of features and the feature dimension of the neural tangent kernel,thus improved the computational efficiency of the multiple kernel learning method.The kernel target alignment was applied to compute the base kernel weight for each approximate neural tangent kernel,and a linear combination of multiple kernels was performed based on the weight to obtain the final multiple kernel decision function.By compareing the single-layer kernel with the NTK-based SVM in 3 UCI datasets,the NTK-based SVM improved the accuracy at least 1.9%,precision at least 2.0%,and recall rate at least 2.0%.By compareing the NS-MKL with other MKL methods in 3 UCI datasets,the NS-MKL improved the accuracy at least 2.0%,and runtime reduced at least 9 s.The proposed algorithm had higher predictive accuracy and faster computation speed.
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