Research Area:  Machine Learning

Abstract:

With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.

Keywords:  
Deep neural networks
Multi-view
Sequential data
Spatio-temporal
Machine Learning
Deep Learning

Author(s) Name:  Zhuyang Xie, Yan Yang, Yiling Zhang, Jie Wang & Shengdong Du

Publisher name:  Springer

DOI:  10.1007/s10462-022-10332-z

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s10462-022-10332-z