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Curse of dimensionality

Known as: Curse of dimension, Problem of dimensionality 
The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces (often with hundreds… Expand
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Papers overview

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Highly Cited
2017
Highly Cited
2017
We consider neural networks with a single hidden layer and non-decreasing homogeneous activa-tion functions like the rectified… Expand
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Highly Cited
2010
Highly Cited
2010
Engineering Systems Analysis for Design Richard de Neufville, Joel Clark, and Frank R. Field Massachusetts Institute of… Expand
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Highly Cited
2009
Highly Cited
2009
For $d$-dimensional tensors with possibly large $d>3$, an hierarchical data structure, called the Tree-Tucker format, is… Expand
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Highly Cited
2005
Highly Cited
2005
In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one… Expand
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Highly Cited
2005
Highly Cited
2005
Modern data analysis tools have to work on high-dimensional data, whose components are not independently distributed. High… Expand
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Highly Cited
2004
Highly Cited
2004
  • J. Friedman
  • Data Mining and Knowledge Discovery
  • 2004
  • Corpus ID: 18543237
The classification problem is considered in which an outputvariable y assumes discrete values with respectiveprobabilities that… Expand
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Highly Cited
2000
Highly Cited
2000
The coming century is surely the century of data. A combination of blind faith and serious purpose makes our society invest… Expand
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Highly Cited
1998
Highly Cited
1998
We present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces. For data sets of size n living… Expand
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Highly Cited
1998
Highly Cited
1998
In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spaces. The Pyramid-Technique is… Expand
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Highly Cited
1994
Highly Cited
1994
This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions… Expand
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