ゲノム情報科学研究教育機構  アブストラクト
Date Dec 18, 2017
Speaker Dr. Yingying Xu, Postdoctoral fellow, HIIT (Helsinki Institute of Information Technology), Finland
Title Inverse finite-size scaling for high-dimensional significance analysis
Abstract We propose an efficient procedure for significance determination in high-dimensional dependence learning based on surrogate data testing, termed as inverse finite-size scaling (IFSS). Our IFSS method is based on the discovery of a universal scaling property of random matrices which enables inference about signal behaviour under null hypothesis from much smaller scale surrogate data than the dimensionality of the original data. As a motivating example, we demonstrate the procedure for ultra high-dimensional genome data fitted with Potts models with the order of 10^10 parameters. IFSS reduces the computational complexity of the data testing procedure by several orders of magnitude, making it very efficient for practical purposes. This approach thus holds considerable potential for generalization to other types of complex models.
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