Riemannian Manifold Learning

Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Ri...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2008-05, Vol.30 (5), p.796-809
Hauptverfasser:	Lin, Tong, Zha, Hongbin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Charts Computer science control theory systems Convex and discrete geometry Data analysis Dimensionality reduction Exact sciences and technology Geometry Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image reconstruction Imaging, Three-Dimensional - methods Information Storage and Retrieval - methods Kernel Learning Linear discriminant analysis Machine learning manifold learning manifold reconstruction Manifolds Mathematical models Mathematics Pattern recognition Pattern Recognition, Automated - methods Preserves Principal component analysis Reconstruction Reproducibility of Results Riemannian manifolds Riemannian normal coordinates Sciences and techniques of general use Sensitivity and Specificity Shape Studies
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