2016/10/12 by Makoto Yamada, Yuta Umezu, Yamada, Makoto +5 · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (stat.ML) #Methodology (stat.ME) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1610.03725
openalex publication_date 2016/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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). The novelty of the proposed algorithm is that it can handle non-linearity and/or structured data through kernels. Namely, the proposed algorithm can be used for wider range of applications including nonlinear multi-class classification and multi-variate regressions, while existing PSI algorithms cannot handle them. Through synthetic experiments, we show that the proposed approach can find a set of statistically significant features for both regression and classification problems. Moreover, we apply the hsicInf algorithm to a real-world data, and show that hsicInf can successfully identify important features.