Robust tensor factorization with unknown noise

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	Robust tensor factorization with unknown noise
	Chen XA(陈希爱); Han Z(韩志); Wang Y(王尧); Zhao, Qian ; Meng DY(孟德宇); Tang YD(唐延东)
	2016
会议日期	June 26 - July 1, 2016
会议地点	Las Vegas, NV, United states
页码	5213-5221
英文摘要	Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of tensor factorization has been applied for the recovery of a low dimensional subspace from high dimensional visual data. Generally, the recovery is achieved by minimizing the loss function between the observed data and the factorization representation. Under different assumptions of the noise distribution, the loss functions are in various forms, like L1and L2norms. However, real data are often corrupted by noise with an unknown distribution. Then any specific form of loss function for one specific kind of noise often fails to tackle such real data with unknown noise. In this paper, we propose a tensor factorization algorithm to model the noise as a Mixture of Gaussians (MoG). As MoG has the ability of universally approximating any hybrids of continuous distributions, our algorithm can effectively recover the low dimensional subspace from various forms of noisy observations. The parameters of MoG are estimated under the EM framework and through a new developed algorithm of weighted low-rank tensor factorization (WLRTF). The effectiveness of our algorithm are substantiated by extensive experiments on both of synthetic data and real image data.
源文献作者	2016-January
产权排序	1
会议录	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
会议录出版者	IEEE Computer Society
会议录出版地	Washington, DC
语种	英语
ISSN号	1063-6919
ISBN号	978-1-4673-8851-1
WOS记录号	WOS:000400012305031
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/19197]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Han Z(韩志)
作者单位	1.University of Chinese Academy of Sciences, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China 3.Xi'an Jiaotong University, China
推荐引用方式 GB/T 7714	Chen XA,Han Z,Wang Y,et al. Robust tensor factorization with unknown noise[C]. 见:. Las Vegas, NV, United states. June 26 - July 1, 2016.