Paper Info
Title
Area-to-point kernel regression on streaming data
Abstract
Spatial data streams are often referenced to an areal spatial unit such as a polygon rather than to a precise point location. This is the case when geo-referencing is done by user IP addresses or from a mobile phone cell ID in various location-based service applications. One problem of interest in this case is spatial modelling of various spatially continuous quantities, such as an intensity of the usage of particular service in the area. This paper investigates a machine learning framework that account for area-to-point data processing. The approach is based on so-called vicinal risk minimization principle. It is elaborated in detail for a class of kernel recursive algorithms developed for distributed processing of streaming data. Concrete examples of kernel computations are provided and the method performance is investigated experimentally.
Year
DOI
Venue
2011
10.1145/2064959.2064967
GIS-IWGS
Keywords
Field
DocType
spatial modelling,areal spatial unit,various location-based service application,kernel computation,area-to-point data processing,particular service,kernel recursive,area-to-point kernel regression,concrete example,various spatially continuous quantity,spatial data stream,distributed processing,point location,recursive algorithm,machine learning,location based service,spatial statistics,data processing,kernel regression,spatial data
Spatial analysis,Kernel (linear algebra),Data mining,Polygon,Data processing,Cell ID,Point location,Computer science,Artificial intelligence,Machine learning,Kernel regression,Recursion
Conference
Citations 
PageRank 
References 
1
0.37
1
Authors
2
Name
Order
Citations
PageRank
Alexei Pozdnoukhov121618.87
Christian Kaiser210.37