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MS Final Examination – Zeyu You


Tuesday, June 10, 2014 9:00 AM - 11:00 AM

A Statistical Inference Framework for Finding Recurring Patterns in Large Data with Applications to Energy Management
The problem of finding unknown patterns that are recurring across multiple sets is an interesting topic. 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 single unknown pattern and a statistical modeling approach is established. The problem can also be reformulated as a blindly joint position estimation. Due to the non-convexity of the negative log-likelihood, finding a global optimal solution is a key challenge. Here, we introduce a novel algorithm to estimate the position of 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. Moreover, we present an expectation-maximization algorithm for jointly estimating multiple templates based on a mixture of non-Gaussian distributions. To address the non-convexity of the problem, a robust initialization method is presented and theoretical guarantees are provided. We evaluate the performance of the algorithm on both synthetic data and real-world data consisting of electrical voltage recordings of home appliance activations.

Major Advisor: Raviv Raich
Committee: Xiaoli Fern
Committee: Thinh Nguyen
GCR: Oksana Ostroverkhova 


Kelley Engineering Center (campus map)
3114
Nicole Thompson
1 541 737 3617
Nicole.Thompson at oregonstate.edu
Sch Elect Engr/Comp Sci
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