Dimensionality reduction

Known as: Dimension reduction 
In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under… (More)
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Topic mentions per year

1971-2018
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Highly Cited
2009
Highly Cited
2009
Spectral analysis-based dimensionality reduction algorithms are important and have been popularly applied in data mining and… (More)
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Highly Cited
2007
Highly Cited
2007
A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to… (More)
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Highly Cited
2007
Highly Cited
2007
Dimensionality reduction is among the keys in mining highdimensional data. This paper studies semi-supervised dimensionality… (More)
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Highly Cited
2006
Highly Cited
2006
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar… (More)
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Highly Cited
2003
Highly Cited
2003
One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex… (More)
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Highly Cited
2002
Highly Cited
2002
The visual interpretation of data is an essential step to guide any further processing or decision making. Dimensionality… (More)
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Highly Cited
2001
Highly Cited
2001
Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because… (More)
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Highly Cited
2001
Highly Cited
2001
The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial… (More)
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Highly Cited
2001
Highly Cited
2001
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the… (More)
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Highly Cited
2000
Highly Cited
2000
Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and… (More)
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