报告名称：Cross-view classification Algotithms
主办单位：英国立博官网中文版
报告专家：尤新革
专家所在单位：华中科技大学
报告时间：2019年6月24日(周一)上午10:00-11:30
报告地点：英国立博官网中文版201报告厅
专家简介：尤新革，博士、教授、博士生导师。2004年博士毕业于香港浸会大学,国际电子电气工程协会高级会员，国际电子电气工程协会系统、人与机器协会模式识别技术委员会副主席，曾任和现任IEEE Transactions on Cybernetics, Neurocomputing等国际刊物编委或客座主编，中国计算机学会视觉专委会委员，中国人工智能协会模式识别专委会委员。现任国家防伪工程技术研究中心主任，入选教育部新世纪优秀人才支持计划。长期从事计算机视觉，机器学习与数据挖掘，模式识别，图像与信号处理，小波分析及其应用，生物特征识别与智能防伪等方面研究，近年主持完成国家支撑计划、国际合作重点项目、国家自然科学基金等国家、省部级项目二十余项，先后获湖北省科技进步三等奖，重庆市自然科学二等奖，湖北省自然科学三等奖；在国际权威刊物及国际会议上发表论文120余篇，其中SCI检索80余篇。参与合作撰写生物特征识别英文专著两本，获得授权发明专利20余项；先后担任安全、模式分析与控制论等多个国际学术会议大会主席和程序委员会主席，主持研发多国纸币及票据的多光谱图像图像采集与分析检测、鉴伪、机读识别等多项核心技术，多光谱票据、证照鉴伪检测与机读识别等领域多项核心技术国内领先，通过与国内多家大型金融机具生产厂家的合作，已将相关成果广泛应用清分机、票据、证照鉴伪等产品中。相关产品在四十多家国内外商业银行等获得商业应用。
报告摘要：Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision.
An effective solution to this problem is the multi-view subspace learning (MvSL), which intends to find a common subspace for multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds. To circumvent this drawback, we propose Multi-view Hybrid Embedding (MvHE) and Multi-view Common Component Discriminant Analysis (MvCCDA) algorithm to handle view discrepancy, discriminancy and nonlinearity simultaneously. Extensive experiments demonstrate the overwhelming advantages against the state-of-the-art MvSL based approaches in terms of classification accuracy.
Low-rank Multi-view Subspace Learning (LMvSL) has also shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods fail to address view discrepancy and discriminancy simultaneously and are incapable of handling complicated noise in practice. To alleviate such limitation, we propose Modal Regression based Structured Low-rank Matrix Recovery (MR-SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Moreover, modal regression incorporated into our model ensures that MR-SLMR is robust to complicated noise. Experimental results demonstrate the superiority of MR-SLMR and its robustness to complicated noise.
