2020/04/08 by Yue Wu, Hao Ni, Wu, Yue +6 · 3 citations
Computer Science · Engineering · Mathematics · #60L10 #Algorithm #Artificial intelligence #Computer science #Computer vision #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Geography #Geometry #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematics #Music and Audio Processing #Nonlinear system #Pattern recognition (psychology) #Physics #Position (finance) #Set (abstract data type) #Signal Processing (eess.SP) #Signature (topology) #Transformation (genetics) #Video Analysis and Summarization #Visibility #cs.LG #eess.SP #electronic engineering #information engineering #msc:60L10 #stat.ML
paper · pdf · doi:10.48550/arxiv.2004.04006
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/04/08 · arxiv created 2020/10/08 · arxiv updated 2020/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we put the visibility transformation on a clear theoretical footing and show that this transform is able to embed the effect of the absolute position of the data stream into signature features in a unified and efficient way. The generated feature set is particularly useful in pattern recognition tasks, for its simplifying role in allowing the signature feature set to accommodate nonlinear functions of absolute and relative values.