A more topologically stable locally linear embedding algorithm based on R*-tree

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5012 LNAI, Issue , 2008, Pages 803-812

  Xia, Tian ^a,b   Li, Jintao ^a   Zhang, Yongdong ^a   Tang, Sheng ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

LLE; Manifold learning; Neighborhood size; Nonlinear dimensionality reduction; R* tree

Indexed keywords

LEARNING SYSTEMS; LINEAR SYSTEMS; NONLINEAR SYSTEMS; PARAMETER ESTIMATION; PROBLEM SOLVING;

INPUT PARAMETERS; LINEAR EMBEDDING ALGORITHMS; MANIFOLD LEARNING; NONLINEAR DIMENSIONALITY REDUCTION;

DECISION TREES;

EID: 44649087686     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-68125-0_78     Document Type: Conference Paper

References (11)

1. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323-2326 (2000)
2. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323 (2000)
3. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373-1396 (2003)
4. Balasubramanian, M., Schwartz, E.L., Tenenbaum, J.B., de Silva, V., Langford, J.C.: The ISOMAP algorithm and topological stability. Science 295, 5552.7a (2002)
5. Samko, O., Marshall, A.D., Rosin, P.L.: Selection of the optimal parameter value for the ISOMAP algorithm. Pattern Recognition Letters 27, 968-979 (2006)
6. Kouropteva, O., Okun, O., Pietikainen, M.: Selection of the optimal parameter value for the Locally Linear Embedding algorithm. In: Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery, pp. 359-363 (2002)
7. Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-Tree: an efficient and robust access method for points and rectangles. In: Proceeding ACM SIGMOD International Conference on Management of Data, NJ, USA, pp. 322-331 (1990)
8. http://www.cs.toronto.edu/~roweis/lle/
9. http://www.rtreeportal.org/
10. Chao, S., Hou-kun, H., Lian-wei, Z.: A more topologically/ stable ISOMAP algorithm. Journal of Software 18(4), 869-877 (2007)
11. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. Advances in Neural Information Processing Systems 17, 1601-1608 (2005)