A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians
文献类型：期刊论文
| 作者 | Wang Y(王尧); Han Z(韩志) ; Lin, Lin; Tang YD(唐延东); Chen XA(陈希爱); Meng DY(孟德宇); Zhao Q(赵谦)
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| 刊名 | IEEE Transactions on Neural Networks and Learning Systems
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| 出版日期 | 2018 |
| 关键词 | Expectation–maximization (EM) algorithm generalized weighted low-rank tensor factorization (GWLRTF) mixture of Gaussians (MoG) model tensor factorization |
| ISSN号 | 2162-237X |
| 产权排序 | 1 |
| 通讯作者 | Han Z(韩志) |
| 中文摘要 | The low-rank tensor factorization (LRTF) technique has received increasing attention in many computer vision applications. Compared with the traditional matrix factorization technique, it can better preserve the intrinsic structure information and thus has a better low-dimensional subspace recovery performance. Basically, the desired low-rank tensor is recovered by minimizing the least square loss between the input data and its factorized representation. Since the least square loss is most optimal when the noise follows a Gaussian distribution, L-norm-based methods are designed to deal with outliers. Unfortunately, they may lose their effectiveness when dealing with real data, which are often contaminated by complex noise. In this paper, we consider integrating the noise modeling technique into a generalized weighted LRTF (GWLRTF) procedure. This procedure treats the original issue as an LRTF problem and models the noise using a mixture of Gaussians (MoG), a procedure called MoG GWLRTF. To extend the applicability of the model, two typical tensor factorization operations, i.e., CANDECOMP/PARAFAC factorization and Tucker factorization, are incorporated into the LRTF procedure. Its parameters are updated under the expectation-maximization framework. Extensive experiments indicate the respective advantages of these two versions of MoG GWLRTF in various applications and also demonstrate their effectiveness compared with other competing methods. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/21579]  |
| 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, Xi'an 710049, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Wang Y,Han Z,Lin, Lin,et al. A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians[J]. IEEE Transactions on Neural Networks and Learning Systems,2018. |
| APA | Wang Y.,Han Z.,Lin, Lin.,Tang YD.,Chen XA.,...&Zhao Q.(2018).A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians.IEEE Transactions on Neural Networks and Learning Systems. |
| MLA | Wang Y,et al."A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians".IEEE Transactions on Neural Networks and Learning Systems (2018). |
入库方式： OAI收割
来源：沈阳自动化研究所
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