Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data onto...
49 KB (6,124 words) - 01:22, 19 April 2024
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the...
22 KB (2,349 words) - 02:53, 4 June 2024
Isomap is a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional embedding methods. Isomap is used for computing...
7 KB (907 words) - 15:26, 4 January 2024
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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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...
8 KB (938 words) - 19:46, 13 May 2024
be extended to a nonlinear system, and therefore motivates the use of SSMs in nonlinear dimensionality reduction. Consider a nonlinear ordinary differential...
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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...
135 KB (14,775 words) - 00:31, 1 June 2024
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data...
114 KB (14,281 words) - 00:18, 2 June 2024
vascular walls. Dimension reduction Metamodeling Principal component analysis Singular value decomposition Nonlinear dimensionality reduction System identification...
24 KB (2,778 words) - 03:39, 22 May 2024