ArticleInternational journal of computer vision2021
Learning Extremal Representations with Deep Archetypal Analysis.
Article in International journal of computer vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Functional archetypes in the human gut microbiome reveal metabolic diversity, stability, and influence disease-associated signatures.Microbiome · 2025Article
- Archetype analysis and the PHATE algorithm as methods to describe and visualize pregnant women's levels of physical activity knowledge.BMC public health · 2024Article
- Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.Investigative ophthalmology & visual science · 2024Article
- Neural ADMIXTURE for rapid genomic clustering.Nature computational science · 2023Article
- Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders.PLoS computational biology · 2022Article
- Archetypal Analysis and DEA Model, Their Application on Financial Data and Visualization with PHATE.Entropy (Basel, Switzerland) · 2022Article
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Archetypes represent extreme manifestations of a population with respect to specific characteristic traits or features. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. As mixing of archetypes is performed directly on the input data, linear Archetypal Analysis requires additivity of the input, which is a strong assumption unlikely to hold e.g. in case of image data. To address this problem, we propose learning an appropriate SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11263-020-01390-3.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.