ArticleJournal of the American Statistical Association2015
Clustering High-Dimensional Landmark-based Two-dimensional Shape Data
Article in Journal of the American Statistical Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- S-GMAS: Genome-Wide Mediation Analysis With Brain Subcortical Shape Mediators.Human brain mapping · 2025Article
- The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes.Trends in cognitive sciences · 2019Review
- On the Geodesic Distance in ShapesEntropy (Basel, Switzerland) · 2018Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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 paper 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 fusion Lasso penalty function and
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