Skip to search formSkip to main contentSkip to account menu

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… 
Wikipedia (opens in a new tab)

Related topics

Related topics

49 relations
Apache SparkAutoencoderBackpropagationBias–variance tradeoff

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2011
Highly Cited
2011
A Pareto Corner Search Evolutionary Algorithm and Dimensionality Reduction in Many-Objective Optimization Problems
Many-objective optimization refers to the optimization problems containing large number of objectives, typically more than four… 
Highly Cited
2010
Highly Cited
2010
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are… 
Highly Cited
2010
Highly Cited
2010
Bregman Divergence-Based Regularization for Transfer Subspace Learning
The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of… 
Highly Cited
2009
Highly Cited
2009
Dimension reduction for nonelliptically distributed predictors
Sufficient dimension reduction methods often require stringent conditions on the joint distribution of the predictor, or, when… 
Highly Cited
2007
Highly Cited
2007
An Introduction to Dimensionality Reduction Using Matlab
Dimensionality reduction is an important task in machine learning, for it facilitates classification, compression, and… 
Highly Cited
2007
Highly Cited
2007
Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery
A new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for… 
Highly Cited
2007
Highly Cited
2007
Principal Manifolds for Data Visualization and Dimension Reduction
The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry… 
Highly Cited
2005
Highly Cited
2005
Graph embedding: a general framework for dimensionality reduction
In the last decades, a large family of algorithms - supervised or unsupervised; stemming from statistic or geometry theory - have… 
Review
2005
Review
2005
Feature Selection for Dimensionality Reduction
Dimensionality reduction is a commonly used step in machine learning, especially when dealing with a high dimensional space of… 
Highly Cited
1997
Highly Cited
1997
Dimensionality reduction of unsupervised data
Dimensionality reduction is an important problem for efficient handling of large databases. Many feature selection methods exist… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)