PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Change-Point Detection using Krylov Subspace Learning
Tsuyoshi Ide and Koji Tsuda
In: SDM 2007, 26-28 Apr 2007, MInneapolis, Minnesota.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:3584
Deposited By:Koji Tsuda
Deposited On:13 February 2008