Outlier Detection for Robust Multi-Dimensional Scaling

Multi-dimensional scaling (MDS) plays a central role in data-exploration, dimensionality reduction and visualization. State-of-the-art MDS algorithms are not robust to outliers, yielding significant errors in the embedding even when only a handful of outliers are present. In this paper, we introduce...

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Détails bibliographiques
Publié dans:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 41(2019), 9 vom: 27. Sept., Seite 2273-2279
Auteur principal: Blouvshtein, Leonid (Auteur)
Autres auteurs: Cohen-Or, Daniel
Format: Article en ligne
Langue:English
Publié: 2019
Accès à la collection:IEEE transactions on pattern analysis and machine intelligence
Sujets:Journal Article
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520 |a Multi-dimensional scaling (MDS) plays a central role in data-exploration, dimensionality reduction and visualization. State-of-the-art MDS algorithms are not robust to outliers, yielding significant errors in the embedding even when only a handful of outliers are present. In this paper, we introduce a technique to detect and filter outliers based on geometric reasoning. We test the validity of triangles formed by three points, and mark a triangle as broken if its triangle inequality does not hold. The premise of our work is that unlike inliers, outlier distances tend to break many triangles. Our method is tested and its performance is evaluated on various datasets and distributions of outliers. We demonstrate that for a reasonable amount of outliers, e.g., under 20 percent, our method is effective, and leads to a high embedding quality 
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