{"data":{"id":"10.6084/m9.figshare.c.2061230.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2061230.v1","prefix":"10.6084","suffix":"m9.figshare.c.2061230.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Huang, Chao","givenName":"Chao","familyName":"Huang","affiliation":[],"nameIdentifiers":[]},{"name":"Styner, Martin","givenName":"Martin","familyName":"Styner","affiliation":[],"nameIdentifiers":[]},{"name":"Zhu, Hongtu","givenName":"Hongtu","familyName":"Zhu","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Clustering High-Dimensional Landmark-Based Two-Dimensional Shape Data"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Mental Health"},{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Information and Computing Sciences"},{"subject":"Biological Sciences"},{"subject":"Earth and Environmental Sciences"},{"subject":"Biotechnology"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2019-08-19","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.c.2061230","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"An important goal in image analysis is to cluster and recognize objects of interest according to the shapes of their boundaries. Clustering such objects faces at least four major challenges including a curved shape space, a high-dimensional feature space, a complex spatial correlation structure, and shape variation associated with some covariates (e.g., age or gender). The aim of this article is to develop a penalized model-based clustering framework to cluster landmark-based planar shape data, while explicitly addressing these challenges. Specifically, a mixture of offset-normal shape factor analyzers (MOSFA) is proposed with mixing proportions defined through a regression model (e.g., logistic) and an offset-normal shape distribution in each component for data in the curved shape space. A latent factor analysis model is introduced to explicitly model the complex spatial correlation. A penalized likelihood approach with both adaptive pairwise fused Lasso penalty function and \u003ci\u003eL\u003c/i\u003e\u003csub\u003e2\u003c/sub\u003e penalty function is used to automatically realize variable selection via thresholding and deliver a sparse solution. Our real data analysis has confirmed the excellent finite-sample performance of MOSFA in revealing meaningful clusters in the corpus callosum shape data obtained from the Attention Deficit Hyperactivity Disorder-200 (ADHD-200) study. Supplementary materials for this article are available online.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/collections/Clustering_High_Dimensional_Landmark_Based_Two_Dimensional_Shape_Data/2061230/1","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"cit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