Efficient generalized boundary detection
Abstract
Fast, efficient, and robust compression-based methods for detecting boundaries in arbitrary datasets, including sequences (1D datasets), are desired. The methods, each employing three simple algorithms, approximate the information distance between two adjacent sliding windows within a dataset. One of the algorithms calculates an initial ordered list of subsequences; while a second algorithm updates the ordered list of subsequences by dropping a first entry and appending a last entry rather than calculating completely new ordered lists with each iteration. Large values in the distance metric are indicative of boundary locations. A smoothed z-score or a wavelet-based algorithm may then be used to locate peaks in the distance metric, thereby identifying boundary locations. An adaptive version of the method employs a collection of window sizes and corresponding weighting functions, making it more amenable to real datasets with unknown, complex, and changing structures.
- Inventors:
- Issue Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1987073
- Patent Number(s):
- 11563446
- Application Number:
- 17/219,217
- Assignee:
- National Technology & Engineering Solutions of Sandia, LLC (Albuquerque, NM)
- DOE Contract Number:
- NA0003525
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 03/31/2021
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Ting, Christina, Field, Jr., Richard V., Quach, Tu-Thach, and Bauer, Travis L. Efficient generalized boundary detection. United States: N. p., 2023.
Web.
Ting, Christina, Field, Jr., Richard V., Quach, Tu-Thach, & Bauer, Travis L. Efficient generalized boundary detection. United States.
Ting, Christina, Field, Jr., Richard V., Quach, Tu-Thach, and Bauer, Travis L. Tue .
"Efficient generalized boundary detection". United States. https://www.osti.gov/servlets/purl/1987073.
@article{osti_1987073,
title = {Efficient generalized boundary detection},
author = {Ting, Christina and Field, Jr., Richard V. and Quach, Tu-Thach and Bauer, Travis L.},
abstractNote = {Fast, efficient, and robust compression-based methods for detecting boundaries in arbitrary datasets, including sequences (1D datasets), are desired. The methods, each employing three simple algorithms, approximate the information distance between two adjacent sliding windows within a dataset. One of the algorithms calculates an initial ordered list of subsequences; while a second algorithm updates the ordered list of subsequences by dropping a first entry and appending a last entry rather than calculating completely new ordered lists with each iteration. Large values in the distance metric are indicative of boundary locations. A smoothed z-score or a wavelet-based algorithm may then be used to locate peaks in the distance metric, thereby identifying boundary locations. An adaptive version of the method employs a collection of window sizes and corresponding weighting functions, making it more amenable to real datasets with unknown, complex, and changing structures.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {1}
}
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