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Diffusion map and Nonlinear dimensionality reduction

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Diffusion map and Nonlinear dimensionality reduction

Diffusion map vs. Nonlinear dimensionality reduction

Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by Coifman and Lafon which computes a family of embeddings of a data set into Euclidean space (often low-dimensional) whose coordinates can be computed from the eigenvectors and eigenvalues of a diffusion operator on the data. High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret.

Similarities between Diffusion map and Nonlinear dimensionality reduction

Diffusion map and Nonlinear dimensionality reduction have 9 things in common (in Unionpedia): Dimensionality reduction, Feature extraction, Laplace–Beltrami operator, Manifold, Markov chain, Multidimensional scaling, Nonlinear dimensionality reduction, Principal component analysis, Random walk.

Dimensionality reduction

In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.

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Feature extraction

In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations.

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Laplace–Beltrami operator

In differential geometry, the Laplace operator, named after Pierre-Simon Laplace, can be generalized to operate on functions defined on surfaces in Euclidean space and, more generally, on Riemannian and pseudo-Riemannian manifolds.

Diffusion map and Laplace–Beltrami operator · Laplace–Beltrami operator and Nonlinear dimensionality reduction · See more »

Manifold

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

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Markov chain

A Markov chain is "a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event".

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Multidimensional scaling

Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset.

Diffusion map and Multidimensional scaling · Multidimensional scaling and Nonlinear dimensionality reduction · See more »

Nonlinear dimensionality reduction

High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret.

Diffusion map and Nonlinear dimensionality reduction · Nonlinear dimensionality reduction and Nonlinear dimensionality reduction · See more »

Principal component analysis

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

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Random walk

A random walk is a mathematical object, known as a stochastic or random process, that describes a path that consists of a succession of random steps on some mathematical space such as the integers.

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The list above answers the following questions

Diffusion map and Nonlinear dimensionality reduction Comparison

Diffusion map has 16 relations, while Nonlinear dimensionality reduction has 74. As they have in common 9, the Jaccard index is 10.00% = 9 / (16 + 74).

References

This article shows the relationship between Diffusion map and Nonlinear dimensionality reduction. To access each article from which the information was extracted, please visit:

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