Abstract
Locally linear embedding is a popular manifold learning algorithm for nonlinear dimensionality reduction. However, the success of LLE depends greatly on an input parameter - neighborhood size, and it is still an open problem how to find the optimal value for it. This paper focuses on this parameter, proposes that it should be self-tuning according to local density not a uniform value for all the data as LLE does, and presents a new variant algorithm of LLE, which can effectively prune “short circuit” edges by performing spatial search on the R*-Tree built on the dataset. This pruning leads the original fixed neighborhood size to be a self-tuning value, thus makes our algorithm have more topologically stableness than LLE does. The experiments prove that our idea and method are correct.
This work was supported in part by the National Basic Research Program of China (973 Program, 2007CB311100), the National High Technology and Research Development Program of China (863 Program, 2007AA01Z416), the Knowledge Innovation Project of The Institute of Computing Technology, Chinese Academy of Sciences (20076031).
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Xia, T., Li, J., Zhang, Y., Tang, S. (2008). A More Topologically Stable Locally Linear Embedding Algorithm Based on R*-Tree. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_78
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DOI: https://doi.org/10.1007/978-3-540-68125-0_78
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