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

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

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

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

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

representation (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by other machine...

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

Nonlinear control theory is the area of control theory which deals with systems that are nonlinear, time-variant, or both. Nonlinear dimensionality reduction...

Intrinsic dimension Latent semantic analysis Latent variable model Ordination (statistics) Manifold hypothesis Nonlinear dimensionality reduction Self-organizing...

system can be extended to a nonlinear system, and therefore motivates the use of SSMs in nonlinear dimensionality reduction. SSMs are chiefly employed...

Roweis, Sam T.; Saul, Lawrence K. (22 December 2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–2326...

Aarhus University. p. 125. Roweis, S.T.; Saul, L.K. (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...

1145/1031171.1031284. Roweis, Sam T.; Saul, Lawrence K. (2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–6...

Cluster analysis Nonlinear dimensionality reduction Spectral clustering Schölkopf, Bernhard; Smola, Alex; Müller, Klaus-Robert (1998). "Nonlinear Component Analysis...

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

uses semidefinite programming to perform non-linear dimensionality reduction of high-dimensional vectorial input data. It is motivated by the observation...

Retrieved 2013-07-14. Roweis, Sam T; Saul, Lawrence K (2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. New Series. 290 (5500):...

Multifactor dimensionality reduction (MDR) is a statistical approach, also used in machine learning automatic approaches, for detecting and characterizing...

Aidos, H., & Kaski, S.: Information retrieval perspective to nonlinear dimensionality reduction for data visualization, The Journal of Machine Learning Research...

can be reconstructed by observation Nonlinear dimensionality reduction – Projection of data onto lower-dimensional manifolds Universal space – topological...

mathematics, the Lyapunov–Schmidt reduction or Lyapunov–Schmidt construction is used to study solutions to nonlinear equations in the case when the implicit...

methods such as T-distributed stochastic neighbor embedding and nonlinear dimensionality reduction. The third group includes model-based ordination methods,...

separation Multilinear PCA Multilinear subspace learning Nonlinear dimensionality reduction Orthogonal matrix Signal separation Singular spectrum analysis...

plots Dimensionality reduction: Multidimensional scaling Principal component analysis (PCA) Multilinear PCA Nonlinear dimensionality reduction (NLDR)...

Zhang, Zhenyue; Hongyuan Zha (2004). "Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment". SIAM Journal on Scientific...

Neuroph Niki.ai Noisy channel model Noisy text analytics Nonlinear dimensionality reduction Novelty detection Nuisance variable One-class classification...

dimension reduction (SDR) is a paradigm for analyzing data that combines the ideas of dimension reduction with the concept of sufficiency. Dimension reduction...

learning Canonical correlation Deep learning Multimodal learning Nonlinear dimensionality reduction Guo, Wenzhong; Wang, Jianwen; Wang, Shiping (2019). "Deep...

filtering stage performs dimensionality reduction, reducing the high-dimensional spatio-temporal stimulus space to a low-dimensional feature space, within...