Our method introduces an additional vector called a reference vector for estimating the angle between the two vectors, and approximates the Euclidean distance�...
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Is the Euclidean distance effective in high dimensional spaces?
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Our method introduces an additional vector called a reference vector for estimating the angle between the two vectors, and approximates the Euclidean distance�...
An Effective Method for Approximating the Euclidean Distance in ...
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Abstract. It is crucial to compute the Euclidean distance between two vectors efficiently in high-dimensional space for multimedia information retrieval.
Jun 27, 2021 � Why not use PCA or autoencoder or any other dimensionality reduction method to project the high dimensional data on low dimensional space and�...
Missing: Effective | Show results with:Effective
Feb 18, 2016 � I would recommend to use fixed point calculation using integers and then the distance approximation is already not too complicated.
Missing: Effective Method
This paper presents analysis of applicability and performance of the Euclidean distance in relation to the dimensionality of the space.
May 18, 2014 � If you could easily embed your data in a low-dimensional data space, then Euclidean distance should also work in the full dimensional space.
Jul 9, 2018 � The short answer is no. At high dimensions euclidean distance loses pretty much all meaning. Though it's not something that's the fault of Euclidean distance�...
Apr 22, 2011 � The most popular is Locality-Sensitive Hashing (LSH), which maps a set of points in a high-dimensional space into a set of bins, ie, a hash table.
In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a efficiency and/or effectiveness perspective.