A statistical inference framework for finding recurring patterns in large data with applications to energy management Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/gx41mm25k

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  • We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images or a short DNA code that is repeated across multiple DNA sequences. We first consider a simple problem of finding a single unknown pattern in multiple data sets. For time series data, the problem can also be formulated as a blind joint delay estimation. The non-convex nature of the problem presents a few challenges. Here, we introduce a novel algorithm to estimate the unknown pattern, which is guaranteed to yield an error within a factor of two of that of the optimal solution. Using mixture modeling, we propose a natural extension to the approach that allows the detection of multiple templates placed across multiple sets. Applications to home energy management are considered.
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