Nonlinear Dimensionality Reduction with Local Spline Embedding

116 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.

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, methodology): “Finally, different from previous work preserving local geometry, LSE [103] uses local spline regression to preserve global geometry.”
Local and Global Regressive Mapping for Manifold Learning with Out-of-Sample Extrapolation (2010, methodology): “…manifold learning algorithms, such as ISOMap (Tenenbaum, Silva, & Langford 2000), Local Linear Embedding (LLE) (Roweis & Saul 2000), Laplacian Eigenmap (LE) (Belkin & Niyogi 2003), Local Tangent Space Alignment (LTSA) (Zhang & Zha 2004), Local Spline Embedding (LSE) (Xiang et al. 2009), etc .”
An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition (2018, background): “However, besides the parameter tuning problem in the construction of graph [2], the qualitative affinity measurement usually suffers from the curse of dimensionality [26], As a result, the predetermined data affinity matrix that is separated from the procedure of feature selection might not be the o”
Graph Learning: A Survey (2021, methodology): “The global geometry can be preserved by using local spline regression [77].”
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, methodology): “We can also employ the same strategies as in Locally tangent Space Alignment (LTSA)[37] or Local Spline Embedding (LSE) [38].”