###
Journal of Software:2018.29(4):945-956

多视角数据缺失补全
杨旭,朱振峰,徐美香,张幸幸
(北京交通大学 计算机科学与信息技术学院, 北京 100044;现代信息科学与网络技术北京市重点实验室(北京交通大学), 北京 100044)
Missing View Completion for Multi-View Data
YANG Xu,ZHU Zhen-Feng,XU Mei-Xiang,ZHANG Xing-Xing
(School of Computer Science and Information Technology, Beijing Jiaotong University, Beijing 100044, China;Beijing Key Laboratory of Advanced Information Science and Network Technology(Beijing Jiaotong University), Beijing 100044, China)
Abstract
Chart / table
Reference
Similar Articles
Article :Browse 1869   Download 1660
Received:April 30, 2017    Revised:June 26, 2017
> 中文摘要: 随着信息技术的快速发展,现实生活中不断涌现出大量的多视角数据,由此应运而生的多视角学习已成为机器学习领域的研究热点.然而,在数据获取过程中,由于收集的难度、高额成本或设备故障等问题,往往导致收集到的多视角数据出现视角缺失,这使得一些多视角学习方法无法有效进行.为此,提出一种基于视角相容性的多视角数据缺失补全方法.通过监督的共享子空间学习,获得与每类多视角数据相对应的共享子空间,从而建立视角相容性判别模型.与此同时,基于共享子空间重构误差等同分布的假设,提出了针对视角缺失的多视角数据的共享表征获取方法,实现多视角缺失数据的预补全.在此基础上,进一步通过多元线性回归实现缺失视角的精确补全.此外,还把所提出的视角补全方法拓展到解决含有噪声的多视角数据的降噪问题.在UCI、COIL-20以及人工合成数据集上的实验结果验证了所提算法的有效性.
Abstract:With the rapid development of information technology, massive amounts of multi-view data are constantly emerging in people's daily life. To cope with such situation, multi-view learning has received much attention in the field of machine learning to promote the ability of data understanding. However, due to the difficulties such as high cost and equipment failure in multi-view data collection, part or all of observed values from one view can't be available, which prevents some traditional multi-view learning algorithms from working effectively as expected. This paper focuses on the missing view completion for multi-view data and proposes a view compatibility based completion method. For each class of multi-view data, a corresponding shared subspace is built by means of supervised learning. With the multiple shared subspaces, a view compatibility discrimination model is developed. Meanwhile, assuming that the reconstruction error of each of view of multi-view data in the shared subspace takes the independent identical distribution, an approach is put forward to seek the shared representation of multi-view data with missing view. Thus, the preliminary completion of missing view can be performed. In addition, the multiple linear regression technique is implemented to obtain a more accurate completion. Furthermore, the proposed missing view completion method is enhanced to deal with the case of the denoising of noise-polluted multi-view data. The experimental results on some datasets including UCI and Coil-20 have demonstrated the effectiveness of the proposed missing view completion method for multi-view data.
文章编号:     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61572068,61532005);中央高校基本科研业务费专项基金(2015JBM039) 国家自然科学基金(61572068,61532005);中央高校基本科研业务费专项基金(2015JBM039)
Foundation items:National Natural Science Foundation of China (61572068, 61532005); Fundamental Research Funds for the Central Universities (2015JBM039)
Reference text:

杨旭,朱振峰,徐美香,张幸幸.多视角数据缺失补全.软件学报,2018,29(4):945-956

YANG Xu,ZHU Zhen-Feng,XU Mei-Xiang,ZHANG Xing-Xing.Missing View Completion for Multi-View Data.Journal of Software,2018,29(4):945-956