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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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Related topics

Related topics

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Apache SparkAutoencoderBackpropagationBias–variance tradeoff
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Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
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
Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study
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
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation: Recent Theory and Fast Algorithms via Convex and Nonconvex Optimization
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
Data-Driven Model Reduction and Transfer Operator Approximation
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: A detailed tutorial
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
Authorship Attribution for Social Media Forensics
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
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives
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 by Learning an Invariant Mapping
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
Dimensionality reduction using genetic algorithms
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
Application of Dimensionality Reduction in Recommender System - A Case Study
Abstract : We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software… Expand
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