Machine Learning for Sciences — Nonlinear Feature Selection for High-Dimensional Data

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：BETVLCTOR伟德官方网站、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，现任日本京都大学副教授、RIKEN AIP单位负责人。于2005年在美国科罗拉多州立大学科林斯堡分校获得电子工程硕士学位，2010年在日本综合研究大学院大学获得统计科学博士学位。曾担任东京工业大学博士后研究员、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。