Oct 21, 2008 � One of its solutions is dimensionality reduction that transforms feature vectors in high-dimensional space to those in low-dimensional space [1,�...
To resolve the dimensionality curse, dimensionality reduction methods have been proposed. They map feature vectors in high-dimensional space into vectors in low�...
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What is dimensionality reduction for high dimensional data?
Does Euclidean distance work well for high dimensional data?
Why Euclidean distance fails in high dimensions?
Is cosine similarity good for high dimensional data?
May 18, 2014 � Euclidean distance is L2 norm and by decreasing the value of k in Lk norm we can alleviate the problem of distance in high dimensional data.�...
Jun 27, 2021 � I take the output vectors of a model which have 2048 dimensions, and I am trying to find the distance between this point and another similar�...
Bibliographic details on Dimensionality reduction for similarity search with the Euclidean distance in high-dimensional applications.
This paper presents analysis of applicability and performance of the Euclidean distance in relation to the dimensionality of the space.
Apr 22, 2011 � Basically in high dimensions, data points have large differences between each other. That decreases the relative difference in the distance�...
This paper proposes a novel method for dimensionality reduction based on a function approximating the Euclidean distance, which makes use of the norm and�...
Apr 12, 2024 � Dimensionality reduction techniques map values from a high dimensional space to one with a lower dimension. The result is a space which�...
ABSTRACT. The dimensionality curse has profound effects on the ef- fectiveness of high-dimensional similarity indexing from the performance perspective.