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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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