Hierarchical Representation Learning based spatio-temporal data redundancy reduction
Hierarchical Representation Learning based spatio-temporal data redundancy reduction
复制标题
基于分层表示学习的时空数据冗余减少
DOI：
10.1016/j.neucom.2015.02.099
复制
发表时间：
2016
期刊：
影响因子：
6
通讯作者：
Wu Bin
中科院分区：
文献类型：
--
作者：
Wang Min;Yang Shuyuan;Wu Bin
时空数据具有体量大、冗余度高的特点，需要大量的存储空间和分析计算能力。在本文中，受人类视觉系统（HVS）视觉感知的稀疏、多尺度和层次特征的启发，我们提出了一种新的基于层次表示学习（HRL）的时空数据冗余减少方法。在我们的方法中，可以通过分层和稀疏的自我表示模型以级联方式识别最具信息性和代表性的数据。讨论了所提出方案的并行实现。该方法在一些大容量时空数据上进行了研究，实验结果证明了其效率和优于一些最先进的结果。
Spatio-temporal data is characteristic of large volume and high redundancy, which will require large amounts of space for storage and computing power for analysis. In this paper, inspired by the sparse, multi-scale and hierarchical characteristics of visual perception in the Human Vision System (HVS), we advance a new Hierarchical Representation Learning (HRL) based spatio-temporal data redundancy reduction approach. In our method, the most informative and representative data can be identified in a cascade manner via a hierarchical and sparse self-representation model. The parallelized realization of the proposed scheme is discussed. The proposed method is investigated on some large volume spatio-temporal data, and the experimental results prove its efficiency and superiority to some state-of-the-art results.