Abstract
In this paper, we propose an adaptive incremental nonlinear dimensionality reduction algorithm for data stream in adaptive Self-organizing Isometric Embedding [1][3] framework. Assuming that each sampling point of underlying manifold and its adaptive neighbors [3] can preserve the principal directions of the regions that they reside on, our algorithm need only update the geodesic distances between anchors and all the other points, as well as distances between neighbors of incremental points and all the other points when a new point arrives. Under the above assumption, our algorithms can realize an approximate linear time complexity embedding of incremental points and effectively tradeoff embedding precision and time cost.
This work was supported in part by Sci.-Tech. Development Project of Tianjin (Grant 04310941R) and Applied Basic Research Project of Tianjin (Grant 05YFJMJC11700).
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Yuexian, H., Kefei, G., Pilian, H. (2006). Adaptively Incremental Self-organizing Isometric Embedding. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_109
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DOI: https://doi.org/10.1007/11893028_109
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