Indexing the solution space: a new technique for nearest neighbor search in high-dimensional space

2000 ◽  
Vol 12 (1) ◽  
pp. 45-57 ◽  
Author(s):  
S. Berchtold ◽  
D.A. Keim ◽  
H.-P. Kriegel ◽  
T. Seidl
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Solution Space ◽  
Nearest Neighbor Search ◽  
New Technique ◽  
High Dimensional ◽  
High Dimensional Space ◽  
Neighbor Search ◽  
A New Technique

An Error Minimizing Partitioning Method for the Nearest Neighbor Search

2013 ◽  
Vol 321-324 ◽  
pp. 2165-2170
Author(s):  
Seung Hoon Lee ◽  
Jaek Wang Kim ◽  
Jae Dong Lee ◽  
Jee Hyong Lee
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Computational Cost ◽  
Nearest Neighbor Search ◽  
High Dimensional ◽  
Index Structures ◽  
Cost Index ◽  
Data Set ◽  
High Dimensional Space ◽  
Neighbor Search

The nearest neighbor search in high-dimensional space is an important operation in many applications, such as data mining and multimedia databases. Evaluating similarity in high-dimensional space requires high computational cost; index-structures are frequently used for reducing computational cost. Most of these index-structures are built by partitioning the data set. However, the partitioning approaches potentially have the problem of failing to find the nearest neighbor that is caused by partitions. In this paper, we propose the Error Minimizing Partitioning (EMP) method with a novel tree structure that minimizes the failures of finding the nearest neighbors. EMP divides the data into subsets with considering the distribution of data sets. For partitioning a data set, the proposed method finds the line that minimizes the summation of distance to data points. The method then finds the median of the data set. Finally, our proposed method determines the partitioning hyper-plane that passes the median and is perpendicular to the line. We also make a comparative study between existing methods and the proposed method to verify the effectiveness of our method.

The Automated Risk Estimation for the Navigation of Autonomous Ships by Learning with Navigation Feature

2020 ◽  
pp. 2041003
Author(s):  
Wei Chian Tan ◽  
Kie Hian Chua ◽  
Yanling Wu
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Risk Estimation ◽  
Nearest Neighbor Search ◽  
Mathematical Representation ◽  
Automatic Identification ◽  
Identification System ◽  
North West ◽  
The North ◽  
Neighbor Search

This work presents a data-driven approach for the automated risk estimation of the voyage of a vessel or ship. While the industry is moving from a compliance-based framework with existing rules to a risk-based one, there is also a need to monitor the risk of a vessel from the perspective of the navigation. This is of even higher importance for the case of autonomous ships. Built based on the state-of-the-art mathematical representation, the navigation feature, each existing voyage is transformed into a corresponding series of points in [Formula: see text]-dimensional space. During the stage of pre-processing, given a set of historical Automatic Identification System (AIS) data, those records that belong to the same vessel within a certain period of time are taken as a voyage and mapped to the corresponding space of the navigation feature. After the pre-processing and during the online monitoring, the current trajectory of the vessel is transformed into the corresponding representation in the same way. Based on a nearest-neighbor search scheme, the distance from the nearest neighbor is taken as the risk of the current voyage. In other words, the deviation from the closest route in the historical data is taken as the risk. The developed method has demonstrated encouraging performance on a set of challenging historical AIS data from the Australian Maritime Safety Authority, covering three regions in the Australian territory, namely, the Bass Strait, the Great Australian Bight and the North West.

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