Nonparametric discovery of learning patterns and autism subgroups from therapeutic data

Vellanki,P, Duong,T, Venkatesh,S and Phung,D 2014, Nonparametric discovery of learning patterns and autism subgroups from therapeutic data, in ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, IEEE, Piscataway, N.J., pp. 1828-1833, doi: 10.1109/ICPR.2014.320.

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Title Nonparametric discovery of learning patterns and autism subgroups from therapeutic data
Author(s) Vellanki,P
Duong,T
Venkatesh,SORCID iD for Venkatesh,S orcid.org/0000-0001-8675-6631
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Conference name Pattern Recognition. Conference (22nd : 2014 : Stockholm, Sweden)
Conference location Stockholm, Sweden
Conference dates 2014/8/24 - 2014/8/28
Title of proceedings ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2014
Conference series Pattern Recognition Conference
Start page 1828
End page 1833
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Autism
Bayesian
Learning patterns
Summary Autism Spectrum Disorder (ASD) is growing at a staggering rate, but, little is known about the cause of this condition. Inferring learning patterns from therapeutic performance data, and subsequently clustering ASD children into subgroups, is important to understand this domain, and more importantly to inform evidence-based intervention. However, this data-driven task was difficult in the past due to insufficiency of data to perform reliable analysis. For the first time, using data from a recent application for early intervention in autism (TOBY Play pad), whose download count is now exceeding 4500, we present in this paper the automatic discovery of learning patterns across 32 skills in sensory, imitation and language. We use unsupervised learning methods for this task, but a notorious problem with existing methods is the correct specification of number of patterns in advance, which in our case is even more difficult due to complexity of the data. To this end, we appeal to recent Bayesian nonparametric methods, in particular the use of Bayesian Nonparametric Factor Analysis. This model uses Indian Buffet Process (IBP) as prior on a binary matrix of infinite columns to allocate groups of intervention skills to children. The optimal number of learning patterns as well as subgroup assignments are inferred automatically from data. Our experimental results follow an exploratory approach, present different newly discovered learning patterns. To provide quantitative results, we also report the clustering evaluation against K-means and Nonnegative matrix factorization (NMF). In addition to the novelty of this new problem, we were able to demonstrate the suitability of Bayesian nonparametric models over parametric rivals.
ISBN 9781479952083
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2014.320
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072294

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
2018 ERA Submission
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