Space-Filling in High-Dimensional Sets

Author(s):  
Anatoly Zhigljavsky ◽  
Antanas Žilinskas
2012 ◽  
Vol 113 ◽  
pp. 26-31 ◽  
Author(s):  
J. Santiago ◽  
M. Claeys-Bruno ◽  
M. Sergent

2020 ◽  
Author(s):  
Patrick Erik Bradley ◽  
Markus Wilhelm Jahn

Abstract Space filling curves are widely used in computer science. In particular, Hilbert curves and their generalizations to higher dimension are used as an indexing method because of their nice locality properties. This article generalizes this concept to the systematic construction of $p$-adic versions of Hilbert curves based on special affine transformations of the $p$-adic Gray code and develops a scaled indexing method for data taken from high-dimensional spaces based on these new curves, which with increasing dimension is shown to be less space consuming than the optimal standard static Hilbert curve index. A measure is derived, which allows to assess the local sparsity of a dataset, and is tested on some real-world data.


2010 ◽  
Vol 29 (3) ◽  
pp. 217-238 ◽  
Author(s):  
Mohamed F. Mokbel ◽  
Walid G. Aref

Author(s):  
Roman Vershynin
Keyword(s):  

2014 ◽  
Author(s):  
Curt Burgess ◽  
Sarah Maples

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