Abstract
A novel supervised learning method is proposed in this paper. It is an extension of local tangent space alignment (LTSA) to supervised feature extraction. First LTSA has been improved to be suitable in a changing, dynamic environment, that is, now it can map new data to the embedded low-dimensional space. Next class membership information is introduced to construct local tangent space when data sets contain multiple classes. This method has been applied to a number of data sets for classification and performs well when combined with some simple classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Borg, I., Groenen, P.: Modern Multidimensional Scaling. Springer, Heidelberg (1997)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2000)
DeMers, D., Cottrell, G.: Non-linear Dimensionality Reduction. Advances in Neural Information Processing Systems 5, 580–587 (1993)
Roweis, S., Saul, L.: Nonlinear Dimension Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Tipping, M.E., Bishop, C.: 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. Technical Report CSE-02-019, CSE, Penn State Univ. (2002)
Kouroteva, O., Okun, O., Soriano, M., Marcos, S., Pietikainen, M.: Beyond Locally Linear Embedding Algorithm. Technical Report MVG-01-2002, Machine Vision Group, University of Oulu, Finland (2002)
Blake, C., Merz, C.: Uci Repository of Machine Learning Databases (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, H., Teng, L., Chen, W., Shen, IF. (2005). Supervised Learning on Local Tangent Space. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_87
Download citation
DOI: https://doi.org/10.1007/11427391_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
eBook Packages: Computer ScienceComputer Science (R0)