Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data onto...

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the...

variant. It is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or...

Isomap is a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional embedding methods. Isomap is used for computing...

linear dimensionality reduction methods such as principal component analysis (PCA), diffusion maps are part of the family of nonlinear dimensionality reduction...

be extended to a nonlinear system, and therefore motivates the use of SSMs in nonlinear dimensionality reduction. Consider a nonlinear ordinary differential...

vascular walls. Dimension reduction Metamodeling Principal component analysis Singular value decomposition Nonlinear dimensionality reduction System identification...

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...

Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data...

high-dimensional data sets by considering a few common features. The manifold hypothesis is related to the effectiveness of nonlinear dimensionality reduction...