Post Selection Inference with Kernels

被引:0
|
作者
Yamada, Makoto [1 ,2 ,4 ]
Umezu, Yuta [3 ]
Fukumizu, Kenji [1 ,4 ]
Takeuchi, Ichiro [1 ,3 ]
机构
[1] RIKEN AIP, Chuo City, Japan
[2] JST PRESTO, Saitama, Japan
[3] Nagoya Inst Technol, Nagoya, Aichi, Japan
[4] Inst Stat Math, Tachikawa, Tokyo, Japan
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Finding a set of statistically significant features from complex data (e.g., nonlinear and/or multi-dimensional output data) is important for scientific discovery and has a number of practical applications including biomarker discovery. In this paper, we propose a kernel-based post-selection inference (PSI) algorithm that can find a set of statistically significant features from non-linearly related data. Specifically, our PSI algorithm is based on independence measures, and we call it the Hilbert-Schmidt Independence Criterion (HSIC)-based PSI algorithm (hsicInf). The novelty of hsicInf is that it can handle non-linearity and/or multi-variate/multi-class outputs through kernels. Through synthetic experiments, we show that hsicInf can find a set of statistically significant features for both regression and classification problems. We applied hsicInf to real-world datasets and show that it can successfully identify important features.
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页数:9
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