SettingsDimension reduction
Dimension reduction
In machine learning, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.
In machine learning, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.
Feature extraction
In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction.
In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction.
Feature selection
In machine learning and statistics, feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the techn...
In machine learning and statistics, feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the techn...
Local tangent space alignment
Local tangent space alignment (LTSA) is a method for manifold learning, which can efficiently learn a nonlinear embedding into low-dimensional coordinates from high-dimensional data, and c...
Local tangent space alignment (LTSA) is a method for manifold learning, which can efficiently learn a nonlinear embedding into low-dimensional coordinates from high-dimensional data, and c...
Locality sensitive hashing
Locality Sensitive Hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional data.
Locality Sensitive Hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional data.
Locality-sensitive hashing
Locality Sensitive Hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional data.
Locality Sensitive Hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional data.
Low-rank approximation
In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix and an approximating matrix, subject to a constraint that the...
In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix and an approximating matrix, subject to a constraint that the...
Multifactor dimensionality reduction
Multifactor dimensionality reduction (MDR) is a data mining approach for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent o...
Multifactor dimensionality reduction (MDR) is a data mining approach for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent o...
Multilinear principal component analysis
Multilinear principal component analysis (MPCA) is a mathematical procedure that uses multiple orthogonal transformations to convert a set of multidimensional objects into another set of multi...
Multilinear principal component analysis (MPCA) is a mathematical procedure that uses multiple orthogonal transformations to convert a set of multidimensional objects into another set of multi...
Multilinear principal-component analysis
Multilinear principal-component analysis is a mathematical procedure that uses multiple orthogonal transformations to convert a set of multidimensional objects into another set of multidimensi...
Multilinear principal-component analysis is a mathematical procedure that uses multiple orthogonal transformations to convert a set of multidimensional objects into another set of multidimensi...
Multilinear subspace learning
Multilinear subspace learning (MSL) aims to learn a specific small part of a large space of multidimensional objects having a particular desired property.
Multilinear subspace learning (MSL) aims to learn a specific small part of a large space of multidimensional objects having a particular desired property.
Nonlinear dimensionality reduction
Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction.
Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction.
Sammon projection
Sammon projection or Sammon mapping is an algorithm that maps a high-dimensional space to a space of lower dimensionality (see multidimensional scaling) by trying to preserve the structure...
Sammon projection or Sammon mapping is an algorithm that maps a high-dimensional space to a space of lower dimensionality (see multidimensional scaling) by trying to preserve the structure...
Self-organizing map
A self-organizing map or self-organizing feature map is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional, discretized representa...
A self-organizing map or self-organizing feature map is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional, discretized representa...
Semantic mapping (statistics)
The semantic mapping (SM) is a dimensionality reduction method that extracts new features by clustering the original features in semantic clusters and combining features mapped in the same clust...
The semantic mapping (SM) is a dimensionality reduction method that extracts new features by clustering the original features in semantic clusters and combining features mapped in the same clust...
Semidefinite embedding
Semidefinite embedding or maximum variance unfolding is an algorithm in computer science that uses semidefinite programming to perform non-linear dimensionality reduction of high-dimension...
Semidefinite embedding or maximum variance unfolding is an algorithm in computer science that uses semidefinite programming to perform non-linear dimensionality reduction of high-dimension...
Sliced inverse regression
Sliced inverse regression is a tool for dimension reduction in the field of multivariate statistics.
Sliced inverse regression is a tool for dimension reduction in the field of multivariate statistics.