A novel supervised cluster adjustment method using a fast exact nearest neighbor search algorithm

2015 ◽  
Vol 20 (3) ◽  
pp. 701-715 ◽  
Author(s):  
Ali Zaghian ◽  
Fakhroddin Noorbehbahani
Author(s):  
Levon Arsalanyan ◽  
Hayk Danoyan

The Nearest Neighbor search algorithm considered in this paper is well known (Elias algorithm). It uses error-correcting codes and constructs appropriate hash-coding schemas. These schemas preprocess the data in the form of lists. Each list is contained in some sphere, centered at a code-word. The algorithm is considered for the cases of perfect codes, so the spheres and, consequently, the lists do not intersect. As such codes exist for the limited set of parameters, the algorithm is considered for some other generalizations of perfect codes, and then the same data point may be contained in different lists. A formula of time complexity of the algorithm is obtained for these cases, using coset weight structures of the mentioned codes


2008 ◽  
Vol 164 (3) ◽  
pp. 69-77 ◽  
Author(s):  
Shiro Ajioka ◽  
Satoru Tsuge ◽  
Masami Shishibori ◽  
Kenji Kita

Author(s):  
Mingjie Li ◽  
Ying Zhang ◽  
Yifang Sun ◽  
Wei Wang ◽  
Ivor W. Tsang ◽  
...  

2006 ◽  
Vol 126 (3) ◽  
pp. 353-360 ◽  
Author(s):  
Shiro Ajioka ◽  
Satoru Tsuge ◽  
Masami Shishibori ◽  
Kenji Kita

Author(s):  
Bilegsaikhan Naidan ◽  
Magnus Lie Hetland

This article presents a new approximate index structure, the Bregman hyperplane tree, for indexing the Bregman divergence, aiming to decrease the number of distance computations required at query processing time, by sacrificing some accuracy in the result. The experimental results on various high-dimensional data sets demonstrate that the proposed index structure performs comparably to the state-of-the-art Bregman ball tree in terms of search performance and result quality. Moreover, this method results in a speedup of well over an order of magnitude for index construction. The authors also apply their space partitioning principle to the Bregman ball tree and obtain a new index structure for exact nearest neighbor search that is faster to build and a slightly slower at query processing than the original.


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