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Adaptive Neighborhood Graph for LTSA Learning Algorithm without Free-Parameter

by Xianlin Zou, Qingsheng Zhu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 4
Year of Publication: 2011
Authors: Xianlin Zou, Qingsheng Zhu
10.5120/2348-3070

Xianlin Zou, Qingsheng Zhu . Adaptive Neighborhood Graph for LTSA Learning Algorithm without Free-Parameter. International Journal of Computer Applications. 19, 4 ( April 2011), 28-33. DOI=10.5120/2348-3070

@article{ 10.5120/2348-3070,
author = { Xianlin Zou, Qingsheng Zhu },
title = { Adaptive Neighborhood Graph for LTSA Learning Algorithm without Free-Parameter },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 4 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number4/2348-3070/ },
doi = { 10.5120/2348-3070 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:08.219064+05:30
%A Xianlin Zou
%A Qingsheng Zhu
%T Adaptive Neighborhood Graph for LTSA Learning Algorithm without Free-Parameter
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 4
%P 28-33
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Local Tangent Space Alignment (LTSA) algorithm is a classic local nonlinear manifold learning algorithm based on the information about local neighborhood space, i.e., local tangent space with respect to each point in dataset, which aims at finding the low-dimension intrinsic structure lie in high dimensional data space for the purpose of dimensionality reduction. In this paper, we present a novel learning algorithm, called 3N-LTSA which needs no free parameter in contrast to LTSA by using an adaptive nearest neighborhood graph. Experimental results show that 3N-LTSA algorithm without free parameter performs more practical and simple algorithm than LTSA.

References
  1. J. Tenenbaum, V De Silva and J. C. Langford. A global geometric framework for nonlinear dimension reduction. Science, 290:2319–2323, 2000.
  2. M. Berstein, V de Silva, J. Langford and J. Tenenbaum. Graph approximations to geodesics on embedded manifolds. http://isomap.stanford.edu/BdSLT.pdf, 2000.
  3. V. de Silva and J. B. Tenenbaum. Global versus local methods in nonlineat dimensionality reduction. Advances in Nerual Information Porcessing Systems 15, Cambridge, MA:MIT Press, 2003, 705-712.
  4. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290: 2323–2326, 2000.
  5. L. Saul and S. Roweis, Think Globally, Fit Locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4 (2003) 119-155.
  6. M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373–1396, June 2003
  7. Z. Zhang, H. Zha, Principal manifolds and nonlinear dimensionality reduction via tangent space alignment, SIAM J. Sci. Comput. 26 (1)(2004) 313–338
  8. T. Zhang, J. Yang, D. Zhao, X. Ge. Linear local tangent space alignment and application to face recognition. Neurocomputering 70(2007) 1547-1553. Letters.
  9. X. He, D. Cai, S. Yan, H. Zhang, Neighborhood preserving embedding, in: Proceedings of the 10 IEEE International Conference on Computer Vision, Beijing, China, October 2005, pp. 1208–1213.
  10. G.Hinton, S. Roweis. Stochastic neighbor embedding. Advances in Neural Information Processing Systems 15 (NIPS'02). pp. 857--864
  11. X. He, P. Niyogi, Locality preserving projections, Proceedings of Advances in Neural Information Processing Systems. Cambridge:MIT Press, 2004: 153-160.
  12. Tony Lin, Hongbin Zha, and Sang Uk Lee. Riemannian manifold learning for nonlinear dimensionality reduction. in ECCV 2006, A. Leonardis, H. Bischof, and A. Prinz (Eds.): Part I, LNCS 3951, pp. 44-55, 2006. Springer-Verlag Berlin Heidelberg 2006.
  13. M. Balasubramanian and E. L. Schwartz, The Isomap algorithm and topological stability. Science, 295, 7a(2002).
  14. J. Tenenbaum, V De Silva and J. C. Langford, The isomap algorithm and topological stability--response, Science 295, 7a (2002).
  15. Xianlin Zou, Qingsheng Zhu and Yifu Jin. An adaptive neigborhood graph for LLE algorithm without free parameter. International Journal of Computer Applitions 16(2), February 2011,ISBN: 978-93-80747-56-8, Digital Object Identifier: 10.5120/1984-2673, PP20-23.
  16. Xianlin Zou, Qingsheng Zhu and Ruilong Yang. Natrual nearest neighbor for Isomap algorithm without free-parameter. Advance Materials Research. Vols. 219-220(2011) pp. 994-998. doi: 10.4028
  17. Xiaoming Huo and Andrew K.Smith, Matrix perturbution analysis of local rangent space alignment. Linear Algebra and its Applications 430 (2009) 732-746.
  18. Yongzhou Li, Dayong Luo and Shaoqiang Liu, Orthogonal discriminant linear local tangent space alignment. Neurocomuting 72 (2009) 1319-1323.
  19. J. Shi and J. Malik, Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8,pp. 888-905, Aug. 2000.
  20. S. Yu and J. Shi, “Multiclass Spectral Clustering,” Proc. Int’l Conf. Computer Vision, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Natural Nearest Neighbor(3N) Adaptive Neighborhood Graph Local Tangent Space Alignment (LTSA) Free Parameter Learning Unsupervised learning

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