De-biasing for intrinsic dimension estimation

IEEE Workshop on Statistical Signal Processing Proceedings

Volumn , Issue , 2007, Pages 601-605

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

^a University of Michigan   (United States)

Author keywords

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

Indexed keywords

COMPUTER NETWORKS; IMAGE PROCESSING; IMAGE SEGMENTATION; ROUTERS; SIGNAL PROCESSING; TECHNICAL PRESENTATIONS;

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

CLUSTERING ALGORITHMS;

EID: 47849097719     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSP.2007.4301329     Document Type: Conference Paper

References (10)

1. A.D. Lanterman, "Schwarz, Wallace, and Rissanen: Intertwining themes in theories of model order estimation," International Statistical Review, vol. 69, pp. 185-212, August 2001.
2. F. Camastra and A. Vinciarelli, "Estimating the intrinsic dimension of data with a fractal-based method," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 10, pp. 1404-1407, October 2002.
3. B. Kégl, "Intrinsic dimension estimation using packing numbers," in Neural Information Processing Systems: NIPS, Vancouver, CA, Dec. 2002.
4. J. A. Costa and A. O. Hero, "Geodesic entropic graphs for dimension and entropy estimation in manifold learning," IEEE Trans. on Signal Processing, vol. 52, no. 8, pp. 2210-2221, August 2004.
5. E. Levina and P. Bickel, "Maximum likelihood estimation of intrinsic dimension," in Neural Information Processing Systems: NIPS, Vancouver, CA, Dec. 2004.
6. J. Costa, A. Girotra, and A. Hero, "Estimating local intrinsic dimension with k-nearest neighbor graphs," IEEE Workshop on Statistical Signal Processing (SSP), pp. 417-422, July 2005.
7. M. Carmo, Riemannian geometry, Birkhäuser, Boston, 1992.
8. S.N. Lahiri, Resampling Methods for Dependent Data, Springer, NY, USA, 2003.
9. Y. Vardi and C.-H Zhang, "The multivariate L1-median and associated data depth," Proceedings of the National Academy of Science USA, vol. 97, pp. 1423-1426, 2000.
10. A. Lakhina, M. Crovella, and C. Diot, "Diagnosing network-wide traffic anomalies," Proceedings of ACM SIGCOMM, pp. 219-230, Aug. 2004.