文献类型：会议论文

英文题名：Discriminative Multiview Nonnegative Matrix Factorization with Large Margin for Image Classification

作者：Long, Fei Ou, Weihua Zhang, Kesheng Tan, Yi Xue, Yunhao Li, Gai

通信作者：Ou, WH[1]

机构：[1]Guizhou Inst Technol, Sch Elect & Informat Engn, Guiyang 550003, Guizhou, Peoples R China;[2]Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550025, Guizhou, Peoples R China;[3]Shunde Polytech, Dept Elect & Informat Engn, Foshan 528300, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550025, Guizhou, Peoples R China.

会议论文集：International Conference on Security, Pattern Analysis, and Cybernetics (ICSPAC)

会议日期：DEC 15-17, 2017

会议地点：Shenzhen, PEOPLES R CHINA

语种：英文

外文关键词：Factorization - Image enhancement - Matrix algebra - Quadratic programming

年份：2017

摘要：Image classification has attracted lots of attentions in recent years. To improve classification accuracy, multiple features are usually extracted to represent the context of images, which imposes a challenge for the combination of those features. To address this problem, we present a discriminative nonnegative multi-view learning approach for image classification based on the observation that those features are often nonnegative. For discrimination, we utilize class label as an auxiliary information to learn discriminative common representations through a set of nonnegative basis vectors with large margin. Meanwhile, view consistency constraint is imposed on the low-dimensional representations and correntropy-induced metric (CIM) is adopted for the measurement of reconstruction errors. We utilized half-quadratic optimization technique to solve the optimization problem and obtain an effective multiplicative update rule. Experimental results demonstrate the learned common latent representations by the proposed method are more efficient than other methods.