Geometric Manifold Learning

We present algorithms for analyzing massive and high dimensional data sets motivated by theorems from geometry and topology. Optimization criteria for computing data projections are discussed and skew radial basis functions (sRBFs) for constructing nonlinear mappings with sharp transitions are demon...

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Veröffentlicht in:	IEEE signal processing magazine 2011-03, Vol.28 (2), p.69-76
Hauptverfasser:	Jamshidi, A A, Kirby, M J, Broomhead, D S
Format:	Magazinearticle
Sprache:	eng
Schlagworte:	Algorithms Data models Dynamical systems Geometry Learning Learning systems Manifolds Mathematical model Mathematical models Nonlinearity Optimization Signal processing algorithms Theorems Time series analysis
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