  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
$20\%$, our method is effective, and leads to a high embedding quality.
