Anchor PCA
Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques. We study PCA for data from multiple related domains. Since principal components generally differ across domains, one way to obtain a shared low-rank embedding is to perform PCA on the pooled data. However, this approach can focus on spurious directions that exhibit high variation in only a few domains. To find a robust embedding that still explains most variance in unseen but similar domains, we propose instead to focus on shared directions of variation. To this end, we introduce Anchor PCA which trades off overall explained variance with agreement between the shared and domain-s...
| Authors | Benedikt Seiter, Anya Fries, Julius von Kügelgen et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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