Variance reduction with neighborhood smoothing for local intrinsic dimension estimation

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2008, Pages 3917-3920

  Carter, Kevin M ^a   Hero III, Alfred O ^a  

^a University of Michigan   (United States)

Author keywords

Geodesies; Intrinsic dimension; Manifold learning; Nearest neighbor graph; Riemannian manifold

Indexed keywords

GEODESIES; INTRINSIC DIMENSION; MANIFOLD LEARNING; NEAREST NEIGHBOR GRAPH; RIEMANNIAN MANIFOLD;

ACOUSTICS; COMPUTER NETWORKS; FINANCIAL DATA PROCESSING; IMAGE PROCESSING; ROUTERS; SIGNAL PROCESSING; SPEECH;

ADAPTIVE ALGORITHMS;

EID: 51449092462     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2008.4518510     Document Type: Conference Paper

References (7)

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4. J. Costa and A. O. Hero, Statistics and analysis of shapes, chapter Learning Intrinsic Dimension and Entropy of High-Dimensional Shape Spaces, pp. 231-252, Birkhauser, 2006.
5. E. Levina and P. Bickel, "Maximum likelihood estimation of intrinsic dimension," in Neural Information Processing Systems: NIPS, Vancouver, CA, Dec. 2004.
6. Leo Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
7. J. H. Friedman and B. E. Popescu, "Predictive learning via rule ensembles," Tech. Rep., Stanford University, 2005.