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Dimensionality reduction

Known as: Dimension reduction, Reduction 
In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under… Expand
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Papers overview

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Review
2018
Review
2018
Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is… Expand
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Review
2018
Review
2018
Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral… Expand
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Review
2018
Review
2018
Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative… Expand
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Review
2018
Review
2018
In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based… Expand
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Review
2017
Review
2017
Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a preprocessing step for… Expand
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Review
2017
Review
2017
The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots, and distributed networks like Tor… Expand
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Review
2017
Review
2017
This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor… Expand
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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… Expand
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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… Expand
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Highly Cited
2000
Highly Cited
2000
Abstract : We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software… Expand
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