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.