Abstract
Low rank approximation methods, e.g. the Nyström method, are often used to speed up eigen-decomposition of kernel matrices. However, it cannot effectively update the extracted subspaces when datasets dynamically increase with time. In this paper, we propose an incremental Nyström method for dynamic learning. Experimental results demonstrate the feasibility and effectiveness of the proposed method.
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Li, H., Zhang, L. (2011). Dynamic Subspace Update with Incremental Nyström Approximation. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22819-3_39
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DOI: https://doi.org/10.1007/978-3-642-22819-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22818-6
Online ISBN: 978-3-642-22819-3
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