Robust tensor factorization with MRF under complex noise
文献类型：会议论文
| 作者 | Wang Y(王尧); Chen XA(陈希爱) ; Han Z(韩志); Shen GP(沈贵萍); Tang YD(唐延东)
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| 出版日期 | 2017 |
| 会议名称 | 7th Annual IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017) |
| 会议日期 | July 31 - August 4, 2017 |
| 会议地点 | Hawaii, USA |
| 页码 | 37-41 |
| 通讯作者 | Han Z(韩志) |
| 中文摘要 | Because of the limitations of matrix factorization, such as losing spatial structure information, the concept of low-rank tensor factorization (LRTF) has been applied for the recovery of a low dimensional subspace from high dimensional visual data. However, existing methods often fail to tackle the real data which are corrupted by the noise with unknown distribution. In this paper, we propose a novel noise model to the tensor case for the LRTF task to overcome the drawbacks of existing models. This procedure treats the target data as high-order tensor directly and models the noise by a Mixture of Gaussians and a Markov Random Field, which is called MoG WLRTF MRF. The parameters in the model are estimated under the variational EM framework. Extensive experiments demonstrate the effectiveness of our method compared with other competing methods. |
| 产权排序 | 1 |
| 会议录 | 7th Annual IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-5386-0489-2 |
| 源URL | [http://ir.sia.cn/handle/173321/21351]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 作者单位 | 1.School of Mathematics and Statistics, Xi’an Jiaotong University 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 3.University of Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Wang Y,Chen XA,Han Z,et al. Robust tensor factorization with MRF under complex noise[C]. 见:7th Annual IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017). Hawaii, USA. July 31 - August 4, 2017. |
入库方式： OAI收割
来源：沈阳自动化研究所
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