Abstract
Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method. It make use of the class membership of each data to be trained in the case of multiple classes, to improve the quality of classification. Furthermore we present how to determine the related parameters for classification and apply this method to a number of artificial and realistic data. Experimental results show that supervised LTSA is superior for classification to other popular methods of dimension reduction when combined with simple classifiers such as the k-nearest neighbor classifier.
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Devijver, P., Kittler, J.: Pattern recognition, a statistical approach. Prentice-Hall, London (1982)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley & Sons, New York (2001)
Jain, A., Duin, R., Mao, J.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Kohonen, T.: Self-organizing Maps, 3rd edn. Springer, Heidelberg (2000)
Bishop, C., Svensén, M., Williams, C.: Gtm: The generative topographic mapping. Neural Computation 10, 215–234 (1998)
Hastie, T., Stuetzle, W.: Principal curves. J. Am. Statistical Assoc. 84 (1988)
DeMers, D., Cottrell, G.: Non-linear dimensionality reduction. In: Giles, C.L., Hanson, S.J., Cowan, J.D. (eds.) Advances in Neural Information Processing Systems 5, San Mateo, CA, pp. 580–587. Morgan Kaufmann, San Francisco (1993)
Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analyzers. Neural Computation 11, 443–482 (1999)
Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimension reduction via local tangent space alignment. SIAM Journal of Scientific Computing 26, 313–338 (2004)
Roweis, S., Saul, L.: Nonlinear dimension reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Li, H., Teng, L., Chen, W., Shen, I.-F.: Supervised learning on local tangent space. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 546–551. Springer, Heidelberg (2005)
Li, H., Chen, W., Shen, I.-F.: Supervised local tangent space alignment for classification. In: IJCAI 2005, poster paper (2005) (to appear)
Blake, C., Merz, C.: Uci repository of machine learning databases (1998)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 13, 71–86 (1991)
de Ridder, D., Duin, R.: Locally linear embedding for classification. Technical Report PH-2002-01, Pattern Recogniion Group, Dept. of Imaging Science and Technology, Delft University of Technology, Delft, The Netherlands (2002)
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Li, H., Chen, W., Shen, IF. (2005). Supervised Learning for Classification. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_7
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DOI: https://doi.org/10.1007/11540007_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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