Degree-preserving randomization

Known as: Degree Preserving Randomization 
Degree Preserving Randomization is a technique used in Network Science that aims to assess whether or not variations observed in a given graph could… (More)
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Topic mentions per year

Topic mentions per year

1975-2017
051019752017

Papers overview

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2015
2015
Compared with supervised feature selection, selecting features in unsupervised learning scenarios is a much harder problem due to… (More)
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2014
2014
The static one-to-one binding of hosts to IP addresses allows adversaries to conduct thorough reconnaissance in order to discover… (More)
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2009
2009
Scale Invariant Feature Transform (SIFT) has shown to be very powerful for general object detection/recognition. And recently, it… (More)
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Highly Cited
2007
Highly Cited
2007
This paper considers the problem of dimensionality reduction by orthogonal projection techniques. The main feature of the… (More)
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2007
2007
In this paper, we propose a new algorithm called Orthogonal Neighborhood Preserving Embedding (ONPE) for face recognition. ONPE… (More)
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2006
2006
Over the past few years, some embedding methods have been proposed for feature extraction and dimensionality reduction in various… (More)
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Highly Cited
2005
Highly Cited
2005
Randomization has emerged as a useful technique for data disguising in privacy-preserving data mining. Its privacy properties… (More)
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Highly Cited
2001
Highly Cited
2001
Past research on art gallery problems has concentrated almost exclusively on bounds on the numbers of guards needed in the worst… (More)
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Highly Cited
2000
Highly Cited
2000
 
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Highly Cited
1998
Highly Cited
1998
We present an algorithm that uses decision procedures to generate finite-state abstractions of possibly infinite-state systems… (More)
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