NDLI: Mixture modeling and inference for recognition of multiple recurring unknown patterns

Content Provider	IEEE Xplore Digital Library
Author	Zeyu You Raich, R. Yonghong Huang
Copyright Year	2014
Description	Author affiliation: Intel Labs., Hillsboro, OR, USA (Yonghong Huang) \|\| Sch. of Electr. Eng. & Comput. Sci., Oregon State Univ. Corvallis, Corvallis, OR, USA (Zeyu You; Raich, R.)
Abstract	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. Earlier work on the topic includes a statistical modeling approach in which the same template is placed at a random position in multiple independent sets. Using mixture modeling, we propose an 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. Our results indicate that the proposed algorithm significantly improves the detection accuracy relative to the single pattern model.
Starting Page	2556
Ending Page	2563
File Size	1103853
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889861
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Minimization DNA Pattern matching Maximum likelihood estimation Upper bound
Content Type	Text
Resource Type	Article

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