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Dimensionality reduction
Known as:
Dimension reduction
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In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under…Â
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Combinatorial optimization
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Machine learning
Related mentions per year
Related mentions per year
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Dimensionality reduction
Statistical classification
Machine learning
Feature extraction
Deep learning
Principal component analysis
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2009
Highly Cited
2009
Patch Alignment for Dimensionality Reduction
Tianhao Zhang
,
Dacheng Tao
,
Xuelong Li
,
Jie Yang
IEEE Transactions on Knowledge and Data…
2009
Spectral analysis-based dimensionality reduction algorithms are important and have been popularly applied in data mining and…Â
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Highly Cited
2007
Highly Cited
2007
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
Shuicheng Yan
,
Dong Xu
,
Benyu Zhang
,
HongJiang Zhang
,
Qiang Yang
,
Stephen Lin
IEEE Transactions on Pattern Analysis and Machine…
2007
A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to…Â
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Highly Cited
2007
Highly Cited
2007
Semi-Supervised Dimensionality Reduction
Daoqiang Zhang
,
Zhi-Hua Zhou
,
Songcan Chen
SDM
2007
Dimensionality reduction is among the keys in mining highdimensional data. This paper studies semi-supervised dimensionality…Â
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Highly Cited
2006
Highly Cited
2006
Dimensionality Reduction by Learning an Invariant Mapping
Raia Hadsell
,
Sumit Chopra
,
Yann LeCun
2006 IEEE Computer Society Conference on Computer…
2006
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar…Â
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Highly Cited
2003
Highly Cited
2003
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Mikhail Belkin
,
Partha Niyogi
Neural Computation
2003
One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex…Â
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Highly Cited
2002
Highly Cited
2002
Nonlinear Dimensionality Reduction
Michel Verleysen
2002
The visual interpretation of data is an essential step to guide any further processing or decision making. Dimensionality…Â
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Highly Cited
2001
Highly Cited
2001
Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases
Eamonn J. Keogh
,
Kaushik Chakrabarti
,
Sharad Mehrotra
,
Michael J. Pazzani
SIGMOD Conference
2001
Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because…Â
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Highly Cited
2001
Highly Cited
2001
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
Eamonn J. Keogh
,
Kaushik Chakrabarti
,
Michael J. Pazzani
,
Sharad Mehrotra
Knowledge and Information Systems
2001
The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial…Â
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Highly Cited
2001
Highly Cited
2001
Random projection in dimensionality reduction: applications to image and text data
Ella Bingham
,
Heikki Mannila
KDD
2001
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the…Â
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Highly Cited
2000
Highly Cited
2000
Dimensionality reduction using genetic algorithms
Michael L. Raymer
,
William F. Punch
,
Erik D. Goodman
,
Leslie A. Kuhn
,
Anil K. Jain
IEEE Trans. Evolutionary Computation
2000
Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and…Â
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