Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi. Post Selection Inference with Kernels.

We propose a novel kernel based post selection inference (PSI) algorithm, which can not only handle non-linearity in data but also structured output such as multi-dimensional and multi-label outputs. Specifically, we develop a PSI algorithm for independence measures, and propose the Hilbert-Schmidt Independence Criterion (HSIC) based PSI algorithm (hsicInf). We apply the hsicInf algorithm to a real-world data, and show that hsicInf can successfully identify important features.