Change Detection: A functional analysis perspective
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We develop a new approach for detecting changes in the behavior of stochastic processes and random fields based on tensor product representations such as the Karhunen-Loève expansion. From the associated eigenspaces of the covariance operator a series of nested function spaces are constructed, allowing detection of signals lying in orthogonal subspaces. In particular this can succeed even if the stochastic behavior of the signal changes either in a global or local sense. A mathematical approach is developed to locate and measure sizes of extraneous components based on construction of multilevel nested subspaces. We show examples in ℝ and on a spherical domain 𝕊^2. However, the method is flexible, allowing the detection of orthogonal signals on general topologies, including spatio-temporal domains.
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