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Citation: | Zhi Han, De-Yu Meng, Zong-Ben Xu, Nan-Nan Gu. Incremental Alignment Manifold Learning[J]. Journal of Computer Science and Technology, 2011, 26(1): 153-165. DOI: 10.1007/s11390-011-1118-7 |
[1] |
Donoho D L. High-dimensional data analysis: The curses and lessings of dimensionality. American Math. Society Lecture, atch Challenges of the 21st Century, 2000.
|
[2] |
Roweis S T, Saul L K. Nonlinear dimensionality reduction y locally linear embedding. Science, Dec. 2000, 290(5500): 323-2326.
|
[3] |
Tenenbaum J B, de Silva V, Langford J C. A global geometric ramework for nonlinear dimensionality reduction. Science, Dec. 2000, 290(5500): 2319-2323.
|
[4] |
Bachmann C M, Ainsworth T L, Fusina R A. Exploiting manfold geometry in hyperspectral imagery. IEEE Trans. Geocience and Remote Sensing, Mar. 2005, 43(3): 441-454.
|
[5] |
Lee J G, Zhang C S. Classification of gene-expression data: he manifold-based metric learning way. Pattern Recogniion, Dec. 2006, 39(12): 2450-2463.
|
[6] |
Shin Y. Facial expression recognition of various internal states ia manifold learning. Journal of Computer Science and echnology, Jul. 2009, 24(4): 745-752.
|
[7] |
Belkin M, Niyogi P. Laplacian eigenmaps for dimensionalty reduction and data representation. Neural Computation, 2003, 15(6): 1373-1396.
|
[8] |
Zhang Z, Zha H. Principal manifolds and nonlinear dimenion reduction via local tangent space alignment. SIAM J. cientific Computing, 2005, 26(1): 313-338.
|
[9] |
Donoho D L, Grimes C. Hessian eigenmaps: New locally linar embedding techniques for high-dimensional data. Proc. he National Academy of Sciences, 2003, 100(10): 5591-5596.
|
[10] |
Weinberger K, Saul L. Unsupervised learning of image maniolds by semidefinite programming. In Proc. IEEE Int. Conf. omputer Vision and Pattern Recognition, Washington DC, SA, Jun. 27-Jul. 2, 2004, pp.988-995.
|
[11] |
Lee J A, Lendasse A, Verleysen M. Nonlinear projection with urvilinear distances: ISOMAP versus curvilinear distance nalysis. Neurocomputing, Mar. 2004, 57: 49-76.
|
[12] |
Hinton G, Roweis S. Stochastic neighbor embedding. In Proc. IPS 2002, Vancouver, Canada, Dec. 9-14, 2002, pp.833-840.
|
[13] |
Agrafiotis D K, Xu H. A self-organizing principle for learning onlinear manifolds. Proceedings of the National Academy of Sciences, 2002, 99(25): 15869-15872.
|
[14] |
Yang L. Alignment of overlapping locally scaled patches for ultidimensional scaling and dimensionality reduction. IEEE rans. Pattern Analysis and Machine Intelligence, Mar. 008, 30(3): 438-450.
|
[15] |
de Silva V, Tenenbaum J B. Global versus local methods in onlinear dimensionality reduction. In Proc. NIPS 2003, ancouver and Whistler, Canada, Dec. 8-13, 2003, pp.705- 12.
|
[16] |
Lin T, Zha H. Riemannian manifold learning. IEEE Trans. Pattern Analysis and Machine Intelligence, May, 2008, 30(5): 96-809.
|
[17] |
Roweis S T, Saul L K, Hinton G E. Global coordination of ocal linear models. In Proc. NIPS 2001, Vancouver, Canada, Dec. 3-8, 2001, pp.889-896.
|
[18] |
Verbeek J. Learning nonlinear image manifolds by global lignment of local linear models. IEEE Trans. Pattern Analsis and Machine Intelligence, Aug. 2006, 28(8): 1236-1250.
|
[19] |
Bachmann C M, Alinsworth T L, Fusina R A. Exploiting anifold geometry in hyperspectral imagery. IEEE Trans. eoscience and Remote Sensing, Mar. 2005, 43(3): 441-454.
|
[20] |
Teh Y W, Roweis S T. Automatic alignment of hidden repreentations. In Proc. NIPS 2002, Vancouver, Canada, Dec. 9- 4, 2002, pp.841-848.
|
[21] |
Verveek J, Roweis S, Vlassis N. Non-linear CCA and PCA by lignment of local models. In Proc. NIPS 2003, Vancouver nd Whistler, Canada, Dec. 8-13, 2003, pp.297-304.
|
[22] |
Zhang T, Yang J, Zhao D, Ge X. Linear local tangent space lignment and application to face recognition. Neuralcomputng, 2007, 70(7-9): 1547-1553.
|
[23] |
Cox T, Cox M. Multidimensional Scaling. Chapman and Hall, 994.
|
[24] |
Law M H C, Zhang N, Jain A K. Nonlinear manifold learning or data stream. In Proc. SIAM Data Mining, Orlando, USA, pr. 22-24, 2004, pp.33-44.
|
[25] |
Law M H C, Jain A K. Incremental nonlinear dimensionality eduction by manifold learning. IEEE Trans. Pattern Analsis and Machine Intelligence, Mar. 2006, 28(3): 377-391.
|
[26] |
Kouropteva O, Okun O, PietikÄainen M. Incremental locally inear embedding. Pattern Recognition, 2005, 38(10): 1764- 767.
|
[27] |
Kouropteva O, Okun O, PietikÄainen M. Incremental locally inear embedding algorithm. In Proc. Fourteenth Scaninavian Conference on Image Analysis, Joensuu, Finland, Jun. 19-22, 2005, pp.521-530.
|
[28] |
Bengio Y, Paiement J F, Vincent P, Delalleau O, Le Roux N, uimet M. Out-of-sample extensions for LLE, Isomap, MDS, igenmaps, and spectral clustering. In Proc. NIPS 2003, ancouver and Whistler, Canada, Dec. 8-13, 2003, pp.177- 84.
|
[29] |
Zhao D, Yang L. Incremental isometric embedding of high diensional data using connected neighborhood graphs. IEEE rans. Pattern Analysis and Machine Intelligence, 2009, 1(1): 86-98.
|
[30] |
Jolliffe I T. Principal Component Analysis. Springer-Verlag, 986.
|
[31] |
Yang J, Zhang D, Frangi A, Yang J. Two-dimentional PCA: new approach to appearance-based face representation and ecognition. IEEE Trans. Pattern Analysis and Machine Inelligence, Jan. 2004, 26(1): 131-137.
|
[32] |
Meng D, Leung Y, Fung T, Xu Z. Nonlinear dimensionality eduction of data lying on the multi-cluster manifold. IEEE rans. Systems, Man and Cybernetics, Part B, Aug. 2008, 8(4): 1111-1122.
|
[33] |
Meng D, Leung Y, Xu Z, Fung T, Zhang Q. Improving eodesic distance estimation based on locally linear assumpion. Pattern Recognition Letters, May 2008, 29(7): 862-870.
|
[34] |
Lee J A, Verleysen M. Nonlinear dimensionality reduction of ata manifolds with essential loops. Neurocomputing, 2005, 7: 29-53.
|
[35] |
Saul L K, Roweis S T. Think globally, fit locally: Unsuperised learning of low dimensional manifold. Journal Machine earning Research, 2003, 4: 119-155.
|
[36] |
Friedman J H, Bentley J L, Finkel R A. An algorithm for findng best matches in logarithmic expected time. ACM Transctions on Mathematical Software, 1977, 3(3): 209-226.
|
[37] |
Nocedal J, Wright S J. Numerical Optimization, 2nd Ed. erlin, New York: Springer-Verlag, 2006, p.24.
|
[38] |
de Silva V, Tenenbaum J B. Global versus local methods in onlinear dimensionality reduction. In Proc. NIPS 2002, ancouver, Canada, Dec. 9-14, 2002, pp.705-712.
|
[1] | Tong Lin, Yao Liu, Bo Wang, Li-Wei Wang, Hong-Bin Zha. Nonlinear Dimensionality Reduction by Local Orthogonality Preserving Alignment[J]. Journal of Computer Science and Technology, 2016, 31(3): 512-524. DOI: 10.1007/s11390-016-1644-4 |
[2] | Young-Suk Shin. Facial Expression Recognition of Various Internal States via Manifold Learning[J]. Journal of Computer Science and Technology, 2009, 24(4): 745-752. |
[3] | XI Haifeng, LUO Yupin, YANG Shiyuan. An Approach to Active Learning for Classifier Systems[J]. Journal of Computer Science and Technology, 1999, 14(4): 372-378. |
[4] | Ma Jiyong, Gao Wen. The Supervised Learning Gaussian Mixture Model[J]. Journal of Computer Science and Technology, 1998, 13(5): 471-474. |
[5] | Dong Yunmei. An Interactive Learning Algorithm for Acquisition of Concepts Represented as CFL[J]. Journal of Computer Science and Technology, 1998, 13(1): 1-8. |
[6] | Yao Shu, Zhang Bo. The Learning Convergence of CMAC in Cyclic Learning[J]. Journal of Computer Science and Technology, 1994, 9(4): 320-328. |
[7] | Ma Zhifang. DKBLM——Deep Knowledge Based Learning Methodology[J]. Journal of Computer Science and Technology, 1993, 8(4): 93-98. |
[8] | Wu Xindong. Inductive Learning[J]. Journal of Computer Science and Technology, 1993, 8(2): 22-36. |
[9] | Harald E. Otto. UNDO, An Aid for Explorative Learning?[J]. Journal of Computer Science and Technology, 1992, 7(3): 226-236. |
[10] | Hayong Zhou. Analogical Learning and Automated Rule Constructions[J]. Journal of Computer Science and Technology, 1991, 6(4): 316-328. |