20160302-06 阚美娜：Multi-view Discriminant Analysis

报告人：阚美娜（计算所）

报告时间：2016年3月2日（星期三）北京时间晚21：00

主持人：王瑞平（计算所）

报告题目: Multi-view Discriminant Analysis

参考文献:

[1] Meina Kan, Shiguang Shan,Haihong Zhang, Shihong Lao, and Xilin Chen. Multi-view Discriminant Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015.

[2] Meina Kan, Shiguang Shan, Haihong Zhang, Shihong Lao, Xilin Chen. Multi-view Discriminant Analysis. European Conference  on Computer Vision (ECCV), 2012.

摘要：In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which  brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this talk, we will  introduce our Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for  multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Specifically, our  MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e.,  maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in  the common space. By reformulating this problem as a ratio trace problem, the multiple linear transforms are achieved  analytically and simultaneously through generalized eigenvalue decomposition.

报告人介绍：阚美娜，博士，毕业于中国科学院计算所，现为计算所副研究员。2014年获得CCF优秀博士学位论文奖以及中科院优秀博士学位论文奖。研究 领域为计算机视觉与模式识别，主要关注人脸识别、多视学习、半监督学习、迁移学习、深度学习等问题，相关成果已发表在TPAMI、IJCV、 TIP、CVPR、ICCV等相关领域主流国际期刊与会议上面。目前担任TPAMI、IJCV、TIP、TMM、TSMC、TNN等多个刊物的审稿人。

报告材料[Slides]