arXiv:2606.06233v1 Announce Type: cross Abstract: 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 direc
The continuous growth of multi-domain and federated data sources necessitates improved dimension reduction techniques that can handle shared and distinct features across these datasets.
This research provides a more robust and efficient method for analyzing complex, distributed datasets, which is crucial for advancing AI applications in diverse fields, particularly those involving sensitive or siloed data.
The proposed 'Anchor PCA' method offers a more stable and interpretable low-rank embedding compared to traditional PCA when dealing with data from multiple related domains, leading to more reliable insights from federated learning and multi-source data analysis.
- · AI researchers
- · Data scientists
- · Industries with multi-domain data (e.g., healthcare, finance)
- · Systems relying solely on pooled PCA for multi-domain data
Improved accuracy and efficiency in unsupervised dimension reduction for complex, distributed datasets.
Faster development and deployment of AI models trained on heterogenous data sources, reducing computational overhead and improving model generalization.
Enhanced scientific discovery and industrial innovation through more effective analysis of integrated, multi-modal, and federated data assets.
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Read at arXiv cs.LG