Incremental manifold learning via tangent space alignment

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

Volumn 4087 LNAI, Issue , 2006, Pages 107-121

  Liu, Xiaoming ^a   Yin, Jianwei ^a   Feng, Zhilin ^a,b   Dong, Jinxiang ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Incremental learning; LASSO; LTSA; Manifold learning

Indexed keywords

APPROXIMATION THEORY; DATA REDUCTION; LEARNING ALGORITHMS; REGRESSION ANALYSIS;

INCREMENTAL LEARNING; LOCAL TANGENT SPACE ALIGNMENT (LTSA); MANIFOLD LEARNING;

DATA STRUCTURES;

EID: 33749385913     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11829898_10     Document Type: Conference Paper

References (14)

1. Seung, S., Daniel, D.L., The manifold ways of perception. Science, 2000, 290(5500): 2268-2269.
2. Tenenbaum, J.B., de Silva, V., and Langford, J.C., A Global Geometrie Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323.
3. Roweis, S.T. and Saul, L.K., Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326.
4. David, L.D., Caroe, G., Hessian eigenmaps Locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences of the United States of America, 2003. 100(10): 5591-5596.
5. Zhang, Z.Y., Zha, H.Y., Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM Journal of Scientific Computing, 2004. 26(1): 313-338.
6. Martin H.C. Law, A.K.J., Incremental Nonlinear Dimensionality Reduction by Manifold Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006. 28(3): p. 377-391.
7. Olga Kouropteva, O.O., Matti Pietikainen, Incremental locally linear embedding. Pattern Recognition, 2005. 38: p. 1764-1767.
8. ZhenYue Zhang, H.Z. A Domain Decomposition Method for Fast Manifold Learning, in Advances in Neural Information Processing Systems. 2006: MIT Press.
9. T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning. Springer, 2001.
10. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least angle regression. Annals of Statistics, 2003.
11. V. de Silva, J.B.T. Global versus Local Approaches to Nonlinear Dimensionality Reduction, in Advances in Neural Information Processing Systems 15. 2003.
12. Jorge Gomes da Suva, J.S.M., Jo?o Manuel Lage de Miranda Lemos. Selecting Landmark Points for Sparse Manifold Learning, in Advances in Neural Information Processing Systems. 2006. Vancouver, Canada: MIT Press.
13. G.H. Golub, C.F.V.L., Matrix Computations. 1996: Johns Hopkins University Press.
14. A. Bjorck, G.H.G., Numerical methods for computing angles between linear subspaces. Mathematics of Computation, 1973. 27(123): p. 579-594.