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...

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

Roweis, Sam T.; Saul, Lawrence K. (22 December 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...

Aarhus University. p. 125. Roweis, S.T.; Saul, L.K. (2000). "Nonlinear dimensionality reduction by locally linear embedding". Science. 290 (5500): 2323–2326...

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

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

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

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

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

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

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

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

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

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

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

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

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

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...

Network (ANN) Connectionism Data mining Machine learning Nonlinear dimensionality reduction Neural network software Pattern recognition Bishop, Svensen...

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

"linear model" is not usually applied. One example of this is nonlinear dimensionality reduction. General linear model Generalized linear model Linear predictor...

Elastic maps provide a tool for nonlinear dimensionality reduction. By their construction, they are a system of elastic springs embedded in the data space...

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

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...