Nonparametric discovery of learning patterns and autism subgroups from therapeutic data

Pratibha Vellanki, Thi Duong, Svetha Venkatesh, Dinh Phung

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

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 22nd International Conference on Pattern Recognition - ICPR 2014
Subtitle of host publication24–28 August 2014 Stockholm, Sweden
EditorsAnders Heyden, Denis Laurendeau, Michael Felsberg
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781479952083, 9781479952090
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Pattern Recognition 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014
Conference number: 22nd


ConferenceInternational Conference on Pattern Recognition 2014
Abbreviated titleICPR 2014
Internet address


  • Autism
  • Bayesian
  • Learning patterns

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