Matrix factorization approach to behavioral mode analysis from acceleration data

Yehezkel S. Resheff, Shay Rotics, Ran Nathan, Daphna Weinshall

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The field of Movement Ecology is experiencing a period of rapid growth in availability of data, and like many other fields is turning to data science for tools and methods to cope with the new challenges and opportunities that this presents. One rich and interesting source of data is the bio-logger. These small electronic devices are attached to animals free to roam in their natural habitats, and report back readings from multiple sensors, including GPS and accelerometer bursts. A common use of this accelerometer data is for supervised learning of behavioral modes. However, there is a need for unsupervised analysis tools as well, due to the inherent difficulties of obtaining a labeled dataset, which in some cases is either infeasible or does not successfully encompass the full repertoire of behavioral modes of interest. Here we present a matrix factorization based clustering method that allows either a soft or a hard partitioning of acceleration measurements, as well as a straight-forward way of drawing insight into the complex movements themselves. The method is validated by comparing the partitions with a labeled dataset, and is further compared to standard methods highlighting the advantages of the new method.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
EditorsGabriella Pasi, James Kwok, Osmar Zaiane, Patrick Gallinari, Eric Gaussier, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467382731
DOIs
StatePublished - 2 Dec 2015
Externally publishedYes
EventIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015 - Paris, France
Duration: 19 Oct 201521 Oct 2015

Publication series

NameProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015

Conference

ConferenceIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
Country/TerritoryFrance
CityParis
Period19/10/1521/10/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems and Management
  • Information Systems

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