Change-Point Detection using Krylov Subspace Learning

Ide, T; Tsuda, K

doi:10.1137/1.9781611972771.54

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Change-Point Detection using Krylov Subspace Learning

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

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Citation

Ide, T., & Tsuda, K. (2007). Change-Point Detection using Krylov Subspace Learning. In C. Apte, D. Skillicorn, B. Liu, & S. Parthasarathy (Eds.), 2007 SIAM International Conference on Data Mining (pp. 515-520). Pittsburgh, PA, USA: Society for Industrial and Applied Mathematics.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CE17-0

Abstract

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.