Multidimensional scaling
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate "information about the pairwise 'distances' among a set of objects or individuals" into a configuration of points mapped into an abstract Cartesian space. More technically, MDS refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. It is a form of non-linear dimensionality reduction.
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- enMultidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate "information about the pairwise 'distances' among a set of objects or individuals" into a configuration of points mapped into an abstract Cartesian space. More technically, MDS refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. It is a form of non-linear dimensionality reduction.
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- enMultidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate "information about the pairwise 'distances' among a set of objects or individuals" into a configuration of points mapped into an abstract Cartesian space. More technically, MDS refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. It is a form of non-linear dimensionality reduction. Given a distance matrix with the distances between each pair of objects in a set, and a chosen number of dimensions, N, an MDS algorithm places each object into N-dimensional space (a lower-dimensional representation) such that the between-object distances are preserved as well as possible. For N = 1, 2, and 3, the resulting points can be visualized on a scatter plot. Core theoretical contributions to MDS were made by James O. Ramsay of McGill University, who is also regarded as the founder of functional data analysis.
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- Multidimensional scaling
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- enMultidimensional scaling
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- CRAN.R-project.org/package=smacof
- CRAN.R-project.org/package=vegan
- scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html
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- Akaike information criterion
- Algorithm
- Bayes factors
- Bayesian information criterion
- Cartesian coordinate system
- Category:Dimension reduction
- Category:Psychometrics
- Category:Quantitative marketing research
- Cayley–Menger determinant
- Centering matrix
- Cross-validation (statistics)
- Data clustering
- Diagonal matrix
- Dimension
- Dimensionality reduction
- Discriminant analysis
- Distance geometry
- Distance matrix
- Eigendecomposition of a matrix
- Eigenvalues and eigenvectors
- ELKI
- Euclidean distance
- Factor analysis
- File:RecentVotes.svg
- Functional data analysis
- Iconography of correlations
- Information visualization
- Isotonic regression
- Jaccard index
- James O. Ramsay
- Likert scale
- Loss function
- Louis Guttman
- MATLAB
- McGill University
- Metric (mathematics)
- Monotonic
- Monte Carlo method
- Non-linear dimensionality reduction
- Non-parametric
- Norm (mathematics)
- Optimization (mathematics)
- Ordination (statistics)
- Perceptual mapping
- R (programming language)
- Real numbers
- R-squared
- Sammon mapping
- Scatter plots
- Scikit-learn
- Semantic differential
- Similarity measure
- Sorenson index
- Stress majorization
- Taxonomy (general)
- SameAs
- 4oVAb
- Escalamiento multidimensional
- Koʻp oʻlchovli masshtablash
- m.023lmj
- Multidimensionale Skalierung
- Nonmetric multidimensional scaling
- Positionnement multidimensionnel
- Q620538
- Scaling multidimensionale
- Skalowanie wielowymiarowe
- Багатовимірне шкалювання
- Многомерное шкалирование
- 多次元尺度構成法
- 多维标度
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- Category:Dimension reduction
- Category:Psychometrics
- Category:Quantitative marketing research
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