Abstract
Improved LLE algorithm is proposed. Usually there is translation in practical data. The form of translation relies on the type of data. Data alignment is a very important step for linear or nonlinear dimensionality reduction methods. Original LLE algorithm uses Euclidean distance to find neighbors, and Euclidean distance is not a good measurement for translated data. Euclidean distance, Hausdorff distance and SSP distance are discussed, and SSP distance is used for improved LLE algorithm. Two methods are put forward for improving LLE algorithm. One is aligning input data of original LLE algorithm, the other is modifying LLE algorithm itself. SSP distance is used to find neighbors, translated data based on SSP distance is used for gaining reconstruction weight, and the plot for each dimension of LLE representation is used for visualization of LLE representation. Motion analysis experiments results show, improved LLE algorithm is better than original LLE algorithm for translated data, and obtains better visualization of LLE representation.
This paper is sponsored by the project (No. 04KJB510167) from the education department of Jiangsu, China.
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References
Roweis, S.T.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Tenebaum, J.B.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. Neural Information Processing Systems 14, 585–591 (2002)
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)
BenAbdelkader, C., Cutler, R., Davis, L.: Motion-based Recognition of People in EigenGait Space. In: Proc. of the Fifth IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 254–259. IEEE Press, New York (2002)
Gross, R., Shi, J.: The CMU Motion of Body (MoBo) Database. Technical Report, CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University (2001)
Li, H., Li, X.: LLE Based Gait Analysis and Recognition. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 671–679. Springer, Heidelberg (2004)
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Li, H., Li, X. (2007). Improved LLE Algorithm for Motion Analysis. In: Xu, M., Zhan, Y., Cao, J., Liu, Y. (eds) Advanced Parallel Processing Technologies. APPT 2007. Lecture Notes in Computer Science, vol 4847. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76837-1_79
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DOI: https://doi.org/10.1007/978-3-540-76837-1_79
Publisher Name: Springer, Berlin, Heidelberg
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