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Change-Point Detection using Krylov Subspace Learning

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Ide, T., & Tsuda, K. (2007). Change-Point Detection using Krylov Subspace Learning. Proceedings of the SIAM International Conference on Data Mining (SDM 2007), 515-520.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CE17-0
Zusammenfassung
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace learning works well both in matrix compression and implicit calculation of the inner product by taking full advantage of the arbitrariness of the seed vector. We apply our algorithm to a PCA-based change-point detection algorithm, and show that it results in about 50 times improvement in computational time.