Dimension Reduction: A Guided Tour

By C. Burges. Found. Trends Mach. Learn., 2010

311 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Optimization with Sparsity-Inducing Penalties (2011, unknown): “, [28] and references therein).”
A Spectral Approach to Gradient Estimation for Implicit Distributions (2018, background): “…et al., 1998), multidimensional scaling (MDS) (Borg & Groenen, 2005), local linear embedding (LLE) (Roweis & Saul, 2000), Laplacian eigenmaps (Belkin & Niyogi, 2003), and spectral clustering (Weiss, 1999), unifying their out-of-sample extensions (Bengio et al., 2004a;b; Burges et al., 2010).”
Applications of machine learning in animal behaviour studies (2017, unknown): “Reducing the dimensionality of a problem is achieved by mapping the original data to a new feature set, feature extraction (Burges, 2009; van Der Maaten, Postma, & van Den Herik, 2009; Lee & Verleysen, 2007), and/or selecting a subset of attributes, feature selection (Liu & Motoda, 2008).”
Linear dimensionality reduction: survey, insights, and generalizations (2014, methodology): “Note also that both PCA and CCA result in a matrix decomposition, and indeed a common approach to many linear dimensionality reduction methods is to attempt to cast the problem as an eigenvalue or generalized eigenvalue problem (Burges, 2010).”
Randomized Matrix Decompositions using R (2016, methodology): “This process aims to eliminate redundancies, while preserving interesting characteristics of the data (Burges 2010).”