Abstract
Since 2000, manifold learning methods have been extensively studied, and demonstrated excellent performance in dimensionality reduction in some application scenarios. However, they still have some drawbacks in approximating real nonlinear relationships during the dimensionality reduction process, thus are unable to retain the original data’s structure well. In this paper, we propose an incremental version of the manifold learning algorithm LTSA based on kernel method, which is called IKLSTA, the abbreviation of Incremental Kernel LTSA. IKLTSA exploits the advantages of kernel method and can detect the explicit mapping from the high-dimensional data points to their low-dimensional embedding coordinates. It is also able to reflect the intrinsic structure of the original high dimensional data more exactly and deal with new data points incrementally. Extensive experiments on both synthetic and real-world data sets validate the effectiveness of the proposed method.
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Supporting webpage. http://www.cs.nyu.edu/~roweis/data.html
Supporting webpage. http://archive.ics.uci.edu/ml/
Acknowledgement
This work was supported by the Program of Shanghai Subject Chief Scientist (15XD1503600) and the Key Projects of Fundamental Research Program of Shanghai Municipal Commission of Science and Technology under grant No. 14JC1400300.
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Tan, C., Guan, J., Zhou, S. (2015). IKLTSA: An Incremental Kernel LTSA Method. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science(), vol 9166. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_5
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DOI: https://doi.org/10.1007/978-3-319-21024-7_5
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