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» Intrinsic Geometries in Learning
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CORR
2010
Springer
92views Education» more  CORR 2010»
13 years 4 months ago
Regression on fixed-rank positive semidefinite matrices: a Riemannian approach
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space...
Gilles Meyer, Silvere Bonnabel, Rodolphe Sepulchre
ICML
2010
IEEE
13 years 8 months ago
Improved Local Coordinate Coding using Local Tangents
Local Coordinate Coding (LCC), introduced in (Yu et al., 2009), is a high dimensional nonlinear learning method that explicitly takes advantage of the geometric structure of the d...
Kai Yu, Tong Zhang
SIGGRAPH
2009
ACM
14 years 2 months ago
Spectral mesh processing
Spectral methods for mesh processing and analysis rely on the eigenvalues, eigenvectors, or eigenspace projections derived from appropriately defined mesh operators to carry out ...
Bruno Lévy, Hao Zhang 0002
GEOINFORMATICA
1998
96views more  GEOINFORMATICA 1998»
13 years 7 months ago
Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization
The nature of map generalization may be non-uniform along the length of an individual line, requiring the application of methods that adapt to the local geometry and the geographi...
Corinne Plazanet, Nara Martini Bigolin, Anne Ruas
CVPR
2007
IEEE
14 years 9 months ago
Learning Visual Similarity Measures for Comparing Never Seen Objects
In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled ...
Eric Nowak, Frédéric Jurie