Prof. Xin Geng

Southeast University, China

Abstract

In the existing machine learning literature, the labels of the training examples are usually just used in the calculation of loss. Most sophisticated operations are actually conducted on the instances, such as feature extraction, feature selection, manifold embedding, dimensionality reduction, etc. Researchers take obviously more efforts in the feature space than in the label space, which is not strange since labels are traditionally represented by logical values, i.e., 1 if the label is relevant to the instance and 0 otherwise. However, if we can somehow transform the logical label vectors into real-valued label vectors, then we can expect much more profound analysis in the label space.

Label distribution learning (LDL) is a recently proposed machine learning paradigm, where each instance is labeled by a real-valued label vector called label distribution. Each element in the label distribution indicates the description degree of the corresponding label to the instance. Considering most existing data sets are annotated by logical labels, we need a way to transform logical labels into label distributions, which is called label enhancement. Label enhancement could unleash the power of label space: many analytic operations meant for the feature space are now applicable to the label space! 

Brief Introduction

Xin Geng is currently a professor and the director of the PALM lab of Southeast University, China. He received the B.Sc. (2001) and M.Sc. (2004) degrees in computer science from Nanjing University, China, and the Ph.D (2008) degree in computer science from Deakin University, Australia. His research interests include machine learning, pattern recognition, and computer vision. He has published over 80 refereed papers in these areas, including those published in prestigious journals and top international conferences. He has been an Associate Editor of IEEE T-MM and FCS, a Steering Committee Member of PRICAI, a Program Committee Chair for conferences such as PRICAI’18, VALSE’13, etc., an Area Chair for conferences such as CVPR’21、ACMMM'18、ICPR’21、WACV’21, and a Senior Program Committee Member for conferences such as IJCAI, AAAI, etc. 

耿新，东南大学特聘教授，计算机科学与工程学院、软件学院院长，人工智能学院执行院长。国际工程与技术学会（IETI）杰出会士，国家基金委优青，江苏省杰青。主要从事模式识别、机器学习、计算机视觉等方面的研究，在这些领域的重要国际学术期刊和会议发表论文90余篇。曾获国家自然科学奖二等奖、国家级教学成果奖一等奖、教育部自然科学奖一等奖等多项教学、科研奖励。现任教育部高校计算机类专业教指委人工智能专家委员会委员，江苏省计算机学会副理事长， CSIG视觉大数据专委会副主任，IEEE计算机学会南京分会副主席，亚太国际人工智能会议（PRICAI）指导委员会委员，CCF人工智能与模式识别专委会常委、计算机视觉专委会常委，中国人工智能学会模式识别专委会常委、机器学习专委会委员，《IEEE T-MM》、《Electronics》《Mathematical Foundations of Computing》等期刊编委，《Frontiers of Computer Science》青年编委。曾任知名国际会议PRICAI’18程序委员会主席，IJCAI、CVPR、ACMMM、ICPR、WACV等重要国际会议领域主席。