Random features¶
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A wrapper of sklearn.kernel_approximation.AdditiveChi2Sampler. |
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Efficient random feature map by Compact Random Feature map. |
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Approximates feature map of the linear kernel by Count Sketch, a.k.a feature hashing. |
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Approximates feature maps of the product between random matrix and feature vectors by FastFood. |
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Approximates feature map of the intersection (min) kernel by explicit feature map, which is proposed by S.Maji and A.C.Berg. |
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Approximates feature map of the RBF or dot kernel by Orthogonal Random Feature map. |
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Approximates feature map of the RBF kernel by Random Fourier Feature map. |
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Approximates feature map of the ANOVA/all-subsets kernel by Random Kernel Feature map. |
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Approximates feature map of a dot product kernel by Monte Carlo approximation of its Maclaurin expansion. |
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Approximates feature map of the linear product kernel by Random Projection. |
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Approximates feature map of the ANOVA kernel by Signed Circulant Random Kernel map. |
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Approximates the product between random matrix and feature vectors by signed circulant random matrix. |
Approximates feature map of the RBF or dot kernel by Structured Orthogonal Random Fourier Feature map. |
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Approximates feature maps of the product between random matrix and feature vectors by Subsampled Randomized Hadamard Transform. |
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Approximates feature map of the ANOVA/all-subsets kernel by Subfeature Random Kernel Feature map. |
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Approximates feature map of a dot product kernel by Monte Carlo approximation of its Maclaurin expansion with only sub features. |
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Approximates feature map of the polynomial kernel by Tensor Sketch. |
Learns importance weights of random features by maximizing the kernel alignment with a divergence constraint. |