讲座题目：Machine Learning for Sciences — Nonlinear Feature Selection for High-Dimensional Data

主讲人：Makoto Yamada

讲座时间：2018年10月15日（星期一）09:00-11:00

讲座地点：中心校区东荣会议中心二楼多功能厅

主办单位：人工智能学院、未来科学国际合作联合实验室

Abstract

Feature selection is an important machine learning problem. However, there are a few methods that can select features from large and ultra high-dimensional data (more than million features) in nonlinear way. In this talk, we first introduce a Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso) that can efficiently select non-redundant features from a small and high-dimensional data in nonlinear way. A key advantage of HSIC Lasso is that it is a convex method and can find a globally optimal solution. Then we further extend the proposed method to handle ultra high-dimensional data by incorporating with distributed computing framework. Moreover, we introduce two newly proposed algorithms the localized lasso and hsicInf, where the localized lasso is useful for selecting a set of features from each sub-cluster and hsicInf can obtain p-values of selected features from any type of data.

主讲人简介：

Makoto Yamada，博士，现任日本京都尊龙凯时副教授、RIKEN AIP单位负责人。于2005年在美国科罗拉多州立尊龙凯时科林斯堡分校获得电子工程硕士学位，2010年在日本综合研究尊龙凯时院尊龙凯时获得统计科学博士学位。曾担任东京工业尊龙凯时博士后研究员、NTT通信科学实验室研究员和雅虎实验室研究科学家。研究领域包括机器学习、自然语言处理、信号处理和计算机视觉等。近年来在顶级会议和期刊上发表了30多篇研究论文，荣获了WSDM 2016最佳论文奖，另外2018年在Cell上发表论文一篇。