Discriminative Dictionary Learning for Autism Spectrum Disorder Identification

Autism Spectrum Disorder (ASD) is a group of lifelong neurodevelopmental disorders with complicated causes. A key symptom of ASD patients is their impaired interpersonal communication ability. Recent study shows that face scanning patterns of individuals with ASD are often different from those of ty...

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Autores principales:	Wenbo Liu, Ming Li, Xiaobing Zou, Bhiksha Raj
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	discriminative dictionary learning autism spectrum disorder mode seeking machine learning eye gaze Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/86e2af5b4a82450ba20cc9a70eca4628
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spelling	oai:doaj.org-article:86e2af5b4a82450ba20cc9a70eca46282021-11-08T06:11:30ZDiscriminative Dictionary Learning for Autism Spectrum Disorder Identification1662-518810.3389/fncom.2021.662401https://doaj.org/article/86e2af5b4a82450ba20cc9a70eca46282021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fncom.2021.662401/fullhttps://doaj.org/toc/1662-5188Autism Spectrum Disorder (ASD) is a group of lifelong neurodevelopmental disorders with complicated causes. A key symptom of ASD patients is their impaired interpersonal communication ability. Recent study shows that face scanning patterns of individuals with ASD are often different from those of typical developing (TD) ones. Such abnormality motivates us to study the feasibility of identifying ASD children based on their face scanning patterns with machine learning methods. In this paper, we consider using the bag-of-words (BoW) model to encode the face scanning patterns, and propose a novel dictionary learning method based on dual mode seeking for better BoW representation. Unlike k-means which is broadly used in conventional BoW models to learn dictionaries, the proposed method captures discriminative information by finding atoms which maximizes both the purity and coverage of belonging samples within one class. Compared to the rich literature of ASD studies from psychology and neural science, our work marks one of the relatively few attempts to directly identify high-functioning ASD children with machine learning methods. Experiments demonstrate the superior performance of our method with considerable gain over several baselines. Although the proposed work is yet too preliminary to directly replace existing autism diagnostic observation schedules in the clinical practice, it shed light on future applications of machine learning methods in early screening of ASD.Wenbo LiuWenbo LiuMing LiMing LiXiaobing ZouBhiksha RajBhiksha RajFrontiers Media S.A.articlediscriminative dictionary learningautism spectrum disordermode seekingmachine learningeye gazeNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Computational Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	discriminative dictionary learning autism spectrum disorder mode seeking machine learning eye gaze Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	discriminative dictionary learning autism spectrum disorder mode seeking machine learning eye gaze Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Wenbo Liu Wenbo Liu Ming Li Ming Li Xiaobing Zou Bhiksha Raj Bhiksha Raj Discriminative Dictionary Learning for Autism Spectrum Disorder Identification
description	Autism Spectrum Disorder (ASD) is a group of lifelong neurodevelopmental disorders with complicated causes. A key symptom of ASD patients is their impaired interpersonal communication ability. Recent study shows that face scanning patterns of individuals with ASD are often different from those of typical developing (TD) ones. Such abnormality motivates us to study the feasibility of identifying ASD children based on their face scanning patterns with machine learning methods. In this paper, we consider using the bag-of-words (BoW) model to encode the face scanning patterns, and propose a novel dictionary learning method based on dual mode seeking for better BoW representation. Unlike k-means which is broadly used in conventional BoW models to learn dictionaries, the proposed method captures discriminative information by finding atoms which maximizes both the purity and coverage of belonging samples within one class. Compared to the rich literature of ASD studies from psychology and neural science, our work marks one of the relatively few attempts to directly identify high-functioning ASD children with machine learning methods. Experiments demonstrate the superior performance of our method with considerable gain over several baselines. Although the proposed work is yet too preliminary to directly replace existing autism diagnostic observation schedules in the clinical practice, it shed light on future applications of machine learning methods in early screening of ASD.
format	article
author	Wenbo Liu Wenbo Liu Ming Li Ming Li Xiaobing Zou Bhiksha Raj Bhiksha Raj
author_facet	Wenbo Liu Wenbo Liu Ming Li Ming Li Xiaobing Zou Bhiksha Raj Bhiksha Raj
author_sort	Wenbo Liu
title	Discriminative Dictionary Learning for Autism Spectrum Disorder Identification
title_short	Discriminative Dictionary Learning for Autism Spectrum Disorder Identification
title_full	Discriminative Dictionary Learning for Autism Spectrum Disorder Identification
title_fullStr	Discriminative Dictionary Learning for Autism Spectrum Disorder Identification
title_full_unstemmed	Discriminative Dictionary Learning for Autism Spectrum Disorder Identification
title_sort	discriminative dictionary learning for autism spectrum disorder identification
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/86e2af5b4a82450ba20cc9a70eca4628
work_keys_str_mv	AT wenboliu discriminativedictionarylearningforautismspectrumdisorderidentification AT wenboliu discriminativedictionarylearningforautismspectrumdisorderidentification AT mingli discriminativedictionarylearningforautismspectrumdisorderidentification AT mingli discriminativedictionarylearningforautismspectrumdisorderidentification AT xiaobingzou discriminativedictionarylearningforautismspectrumdisorderidentification AT bhiksharaj discriminativedictionarylearningforautismspectrumdisorderidentification AT bhiksharaj discriminativedictionarylearningforautismspectrumdisorderidentification
_version_	1718442924545409024

Discriminative Dictionary Learning for Autism Spectrum Disorder Identification

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