Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data, potentially...
48 KB (6,106 words) - 00:59, 19 November 2024
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the...
21 KB (2,252 words) - 22:16, 4 December 2024
Isomap is a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional embedding methods. Isomap is used for computing...
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T-distributed stochastic neighbor embedding (category Dimension reduction)
variant. It is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or...
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vascular walls. Dimension reduction Metamodeling Principal component analysis Singular value decomposition Nonlinear dimensionality reduction System identification...
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linear dimensionality reduction methods such as principal component analysis (PCA), diffusion maps are part of the family of nonlinear dimensionality reduction...
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high-dimensional data sets by considering a few common features. The manifold hypothesis is related to the effectiveness of nonlinear dimensionality reduction...
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Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data...
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Machine learning (section Dimensionality reduction)
ISBN 978-3-642-27644-6. Roweis, Sam T.; Saul, Lawrence K. (22 Dec 2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–2326...
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system can be extended to a nonlinear system, and therefore motivates the use of SSMs in nonlinear dimensionality reduction. SSMs are chiefly employed...
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Aarhus University. p. 125. Roweis, S.T.; Saul, L.K. (2000). "Nonlinear dimensionality reduction by locally linear embedding". Science. 290 (5500): 2323–2326...
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Intrinsic dimension Latent semantic analysis Latent variable model Ordination (statistics) Manifold hypothesis Nonlinear dimensionality reduction Self-organizing...
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plots Dimensionality reduction: Multidimensional scaling Principal component analysis (PCA) Multilinear PCA Nonlinear dimensionality reduction (NLDR)...
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Aidos, H., & Kaski, S.: Information retrieval perspective to nonlinear dimensionality reduction for data visualization, The Journal of Machine Learning Research...
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parameter Nonlinear autoregressive exogenous model Nonlinear dimensionality reduction Non-linear iterative partial least squares Nonlinear regression...
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1145/1031171.1031284. Roweis, Sam T.; Saul, Lawrence K. (2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–6...
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Autoencoder (category Dimension reduction)
representation (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by other machine...
49 KB (6,150 words) - 08:29, 13 December 2024
Nonlinear control theory is the area of control theory which deals with systems that are nonlinear, time-variant, or both. Nonlinear dimensionality reduction...
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Kernel principal component analysis (category Dimension reduction)
Cluster analysis Nonlinear dimensionality reduction Spectral clustering Schölkopf, Bernhard; Smola, Alex; Müller, Klaus-Robert (1998). "Nonlinear Component Analysis...
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(eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering in fewer dimensions. The similarity matrix is...
23 KB (2,933 words) - 07:33, 27 August 2024
Multifactor dimensionality reduction (MDR) is a statistical approach, also used in machine learning automatic approaches, for detecting and characterizing...
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Neuroph Niki.ai Noisy channel model Noisy text analytics Nonlinear dimensionality reduction Novelty detection Nuisance variable One-class classification...
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dimension reduction (SDR) is a paradigm for analyzing data that combines the ideas of dimension reduction with the concept of sufficiency. Dimension reduction...
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Retrieved 2013-07-14. Roweis, Sam T; Saul, Lawrence K (2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. New Series. 290 (5500):...
45 KB (5,098 words) - 11:48, 7 December 2024
Semidefinite embedding (category Dimension reduction)
uses semidefinite programming to perform non-linear dimensionality reduction of high-dimensional vectorial input data. It is motivated by the observation...
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"linear model" is not usually applied. One example of this is nonlinear dimensionality reduction. General linear model Generalized linear model Linear predictor...
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the method of elastic maps, is used for data mining and nonlinear dimensionality reduction. In simple words, "the first term is defined as the error...
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orbital period around the attractor. Whitney embedding theorem Nonlinear dimensionality reduction Sauer, Timothy D. (2006-10-24). "Attractor reconstruction"...
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separation Multilinear PCA Multilinear subspace learning Nonlinear dimensionality reduction Orthogonal matrix Signal separation Singular spectrum analysis...
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analysis Cluster analysis Multiple correspondence analysis Nonlinear dimensionality reduction Robust statistics Heteroskedasticity-consistent standard errors...
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