Incremental locally linear embedding algorithm based on orthogonal iteration method

Tien Tzu Hsueh Pao/Acta Electronica Sinica

Volumn 37, Issue 1, 2009, Pages 132-136

  Zhu, Ming Han ^a,b   Luo, Da Yong ^a   Yi, Li Qun ^b   Wang, Yi Jun ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b HUNAN UNIVERSITY OF ARTS AND SCIENCE   (China)

Author keywords

Increment; Locally linear embedding (LLE); Manifold learning; Orthogonal iteration

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; PARTICLE SIZE ANALYSIS;

BATCH METHODS; COMPUTATIONAL RESULTS; DATA SETS; INCREMENT; LOCALLY LINEAR EMBEDDING (LLE); LOCALLY LINEAR EMBEDDING ALGORITHMS; MANIFOLD LEARNING; MANIFOLD LEARNING ALGORITHMS; ORTHOGONAL ITERATION;

ITERATIVE METHODS;

EID: 61649109298     PISSN: 03722112     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

1. J B Tenenbaum, V de Silva, et al. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.
2. S T Roweis, L K Saul. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.
3. M Belkin, P Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering[A]. Neural Information Processing Systems 14[C]. Cambridge, MA: MIT Press, 2002, 585-591.
4. M H Law, N Zhang, et al. Nonlinear manifold learning for data stream[A]. Proceedings of SIAM Data Mining[C]. Florida: Orlando, 2004. 33-44.
5. X He, S Yan, et al. Face recognition using Laplacianfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
6. L K Saul, S T Roweis. Think globally, fit locally: unsupervised learning of low dimensional manifolds[J]. Journal of Machine Learning Research, 2003, 4(Jun): 119-155.
7. Olga Kouropteva, Oleg Okun et al. Incremental locally linear embedding algorithm[J]. Pattern Recognition, 2005, 38(10): 1764-1767.
8. Zeng Xianhua, Luo Siwei. A dynamically incremental manifold learning algorithm[J]. Journal of Computer Research and Development, 2007, 44(9): 1462-1468. (in Chinese)
9. G H Golub, C F Van Loan. Matrix Computations[M]. Baltimore: Johns Hopkins University Press, 1996. 422-424.
10. Data sets for nonlinear dimensionality reduction[OL] http://isomap.stanford.edu/datasets.html