A new pipeline for interpretable heterogeneous regression that combines response-informed random Fourier features, PCA embedding, GMM soft clustering, and cluster-specific spline GAMs.
and Smola, A.J.,Learning with Kernels: Support Vector Machines, Regulariza- tion, Optimization, and Beyond
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Cluster-Based Generalized Additive Models Informed by Random Fourier Features
A new pipeline for interpretable heterogeneous regression that combines response-informed random Fourier features, PCA embedding, GMM soft clustering, and cluster-specific spline GAMs.