（计算机学院宣 文/图 郭涵）2017年11月30日上午，应计算机科学与技术学院张海军副教授邀请，在第一届数据驱动工业信息学研讨会(The 1st Workshop on Data-driven
Industrial Informatics)上，比利时卢万天主教大学工程学院Michel Verleysen教授（IEEE Fellow）在A509为我院师生做了题为“Nonlinear
Dimensionality Reduction for High-dimensional
Data Analysis”的学术报告。
报告中，Michel Verleysen教授对高维数据分析中的非线性降维技术进行了深入浅出的讲解。首先，Michel Verleysen教授说到降维旨在提供能真实反映原始高维数据固有特性的低维表示，降维是许多科学分支中不可缺少的工具，并介绍了其在诸如社会学、心理测量学、统计学、大数据挖掘中的重要应用。随后，Michel Verleysen教授表明一个高维数据的真实低维表示保留了原始数据的关键属性，如何选择关键属性（欧几里德距离、测地线距离、相似性、……）在很大程度上直接影响结果表示。最后，Michel Verleysen教授重点介绍了依赖于距离，领域以及保存相似性等现代降维方法，以及谱方法和非线性优化工具的使用。
通过本次报告，现场师生们对高维数据分析中的非线性降维有了进一步的理解和深刻的认识。报告过程中，我院师生和Michel Verleysen教授针对报告中的内容进行了交流，Michel Verleysen教授对提出的问题给予了详尽的讲解，现场气氛活跃，学术氛围浓郁。
主讲人简历:
Michel
Verleysen is a Professor of Machine Learning at the Université catholiquede Louvain,
Belgium. He has been an invited professor at the Swiss E.P.F.L. (Ecole Polytechnique Fédérale de
Lausanne, Switzerland), at the Université d'Evry Val d'Essonne (France), at
the Université ParisI-Panthéon-Sorbonne
and at Université Paris Est. He is an Honorary
Research Director of the Belgian F.N.R.S. (National Fund for Scientific
Research), and the Dean of the Louvain School of Engineering. He is
editor-in-chief of the Neural Processing Letters journal (published by
Springer), chairman of the annual ESANN conference (European Symposium on
Artificial Neural Networks, Computational Intelligence and Machine Learning),
past associate editor of the IEEE Trans. on Neural Networks journal, and member
of the editorial board and program committee of several journals and
conferences on neural networks and learning. He was the chairman of the IEEE
Computational Intelligence Society Benelux chapter (2008-2010), and member of
the executive board of the European Neural Networks Society (2005-2010). He is
author or co-author of more than 250 scientific papers in international
journals and books or communications to conferences with reviewing committee.
He is the co-author of the scientific popularization book on artificial neural
networks in the series “Que Sais-Je?”, in French, and of the "Nonlinear
Dimensionality Reduction" book published by Springer in 2007. His research
interests include machine learning, feature selection, nonlinear dimensionality
reduction, visualization, high-dimensional data analysis, self-organization,
time-series forecasting and biomedical signal processing.
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