基于回归的自适应核条件独立性检验

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yixin Ren , Juncai Zhang , Yewei Xia , Ruxin Wang , Feng Xie , Jihong Guan , Hao Zhang , Shuigeng Zhou
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引用次数: 0

摘要

提出了一种新的自适应核回归条件独立(CI)检验框架,将CI检验的任务简化为回归和统计独立性检验,同时证明了在保证回归一致性的前提下,通过自适应学习独立检验的参数化核,可以最大限度地提高CI的检验能力。对于独立性检验的自适应学习核,我们首先通过建模零分布在学习过程中的变化,解决了现有信噪比准则固有的缺陷,然后设计了一类新的核,可以自适应地关注变量的重要维度来判断独立性,这使得测试比使用仅在长度尺度上自适应的简单核更灵活。特别适用于高维复杂数据。理论上,我们证明了所提出的测试的一致性,并表明用于学习的非凸目标函数符合l -平滑条件,从而有利于优化。合成数据和实际数据的实验结果表明了该方法的优越性。源代码和数据集可从https://github.com/hzsiat/AdaRCIT获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regression-based conditional independence test with adaptive kernels
We propose a novel framework for regression-based conditional independence (CI) test with adaptive kernels, where the task of CI test is reduced to regression and statistical independence test while proving that the test power of CI can be maximized by adaptively learning parameterized kernels of the independence test if the consistency of regression can be guaranteed. For the adaptively learning kernel of independence test, we first address the pitfall inherent in the existing signal-to-noise ratio criterion by modeling the change of the null distribution during the learning process, then design a new class of kernels that can adaptively focus on the significant dimensions of variables to judge independence, which makes the tests more flexible than using simple kernels that are adaptive only in length-scale, and especially suitable for high-dimensional complex data. Theoretically, we demonstrate the consistency of the proposed tests, and show that the non-convex objective function used for learning fits the L-smoothing condition, thus benefiting the optimization. Experimental results on both synthetic and real data show the superiority of our method. The source code and datasets are available at https://github.com/hzsiat/AdaRCIT.
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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