基于视图内字典原子不一致的多视图字典学习算法  

Multi-view dictionary learning based on intra-view atom incoherence algorithm

作　　者：田泽 杨明[1] 陈哲[2] 石爱业[2] TIAN Ze;YANG Ming;CHEN Zhe;SHI Aiye(College of Computer Science and Technology,Nanjing Normal University,Nanjing 210023;College of Computer and Information,Hohai University,Nanjing 210098)

机构地区：[1]南京师范大学计算机科学与技术学院,南京210023 [2]河海大学计算机与信息学院,南京210098

出　　处：《南京信息工程大学学报（自然科学版）》2019年第3期309-315,共7页Journal of Nanjing University of Information Science & Technology（Natural Science Edition）

基　　金：国家自然科学基金重点项目(61432008);国家自然科学基金项目(61876087)

摘　　要：传统的多视图字典学习算法旨在利用多视图数据间的相关性,未能考虑多视图数据的差异性,这可能会降低字典的学习性能.受此启发,提出一种基于视图内字典原子不一致的多视图字典学习算法.该算法为每个视图学习类属字典和共享字典,同时,引入编码系数方差的最小化约束,以降低视图间字典的差异性;此外,通过每个视图编码系数与所有视图编码系数均值之间距离的加权和的最小化来约束相应特征的贡献度;然后,施加视图内字典原子的不一致性约束以降低视图内字典的冗余.最后,在两个数据集(AR和Extended Yale B数据集)上的实验验证了所提算法的有效性.The traditional multi-view dictionary learning algorithm is designed to take advantage of the correlation between multi-view data and fails to consider the distinctiveness of the multi-view data,which may reduce the performance of dictionary.Inspired by this observation,we present a multi-view dictionary learning based on the intraview atom inconsistency algorithm.The algorithm learns class-specific dictionaries and the shared class dictionary for each view and calculates the minimum of the coding coefficient variance to reduce the distinctiveness of inter-view dictionaries.In addition,the minimization of the weighted sum of the distance between the coding coefficients between each view and the mean of coding coefficients for all views restrict the contribution of the corresponding features.Then,we embed the inconsistency constraint into the intra-view dictionaries to reduce redundancy.Finally,two datasets ( AR and Extended Yale B datasets) were used to validate the effectiveness of the proposed algorithm.

关 键 词：多视图字典学习 相关性 差异性 人脸识别 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]