ISOLLE: LLE with geodesic distance

Neurocomputing

Volumn 69, Issue 13-15, 2006, Pages 1768-1771

  Varini, Claudio ^a   Degenhard, Andreas ^a   Nattkemper, Tim W ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Geodesic distance; LLE; Nonlinear dimensional data reduction

Indexed keywords

ALGORITHMS; DATA REDUCTION; DATA STRUCTURES; DISTANCE MEASUREMENT; IMAGE RECONSTRUCTION;

GEODESIC DISTANCE; LOCALLY LINEAR EMBEDDING; NONLINEAR DIMENSIONAL DATA REDUCTION;

GEODESY;

ACCURACY; ALGORITHM; ARTICLE; CONTRAST ENHANCEMENT; CONTROLLED STUDY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; VALIDATION PROCESS;

EID: 33745204883     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2005.12.120     Document Type: Article

References (5)

1. Dijkstra E.W. A note on two problems in connection with graphs. Numerische Mathematik 1 (1959) 269-271
2. J.A. Lee, A. Lendasse, N. Donckers, M. Verleysen, A robust nonlinear projection method, in: Proceedings of European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 2000, pp. 13-20.
3. M. Polito, P. Perona, Grouping and dimensionality reduction by locally linear embedding, in: Advances in Neural Information Processing Systems (NIPS) 14, MIT Press, Vancouver, British Columbia, Canada, December 2001.
4. Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 (2000) 2323-2326
5. Tenenbaum J.B., de Silva V., and Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 5500 (2000) 2319-2322