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Nonparametric discovery of learning patterns and autism subgroups from therapeutic data
conference contribution
posted on 2014-12-04, 00:00 authored by Pratibha Vellanki, Thi Duong, Svetha VenkateshSvetha Venkatesh, Quoc-Dinh PhungAutism 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.
History
Event
Pattern Recognition. Conference (22nd : 2014 : Stockholm, Sweden)Pagination
1828 - 1833Publisher
IEEELocation
Stockholm, SwedenPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-08-24End date
2014-08-28ISSN
1051-4651ISBN-13
9781479952083Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICPR 2014 : Proceedings of the 22nd International Conference on Pattern RecognitionUsage metrics
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