期刊
NEUROCOMPUTING
卷 348, 期 -, 页码 3-15出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2018.07.082
关键词
Sequential data modeling; High-dimensional data; Kernel learning; Gaussian process latent variable model
资金
- National Key Research and Development Program of China [2016YFC0800705-2]
- Key Program for International S&T Cooperation Projects of China [2016YFE0121200]
- Provincial Natural Science Fund of Hubei [2016CFB117, 2018CFB195]
- Provincial Education Office Science and Technology Research Project Youth Talent Project of Hubei [Q20161113]
- National Natural Science Foundation of China [61702382]
Modeling sequential data has been a hot research field for decades. One of the most challenge problems in this field is modeling real-world high-dimensional sequential data with limited training samples. This is mainly due to the following two reasons. First, if the dimension of the data is significantly greater then the number of the data, it may result in the over-fitting problem. Second, the dynamic behavior of the real-world data is very complex and difficult to approximate. To overcome these two problems, we propose a multi-kernel Gaussian process latent variable regression model for high-dimensional sequential data modeling and prediction. In our model, we design a regression model based on the Gaussian process latent variable model. Furthermore, a multi-kernel learning model is designed to automatically construct suitable nonlinear kernel for various types of sequential data. We evaluate the effectiveness of our method using two types of real-world high-dimensional sequential data, including the human motion data and the motion texture video data. In addition, our method is compared with several representative sequential data modeling methods. Experimental results show that our method achieves promising modeling capability and is capable of predict human motion and texture video with higher quality. (C) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据