Abstract:
:Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Abid A,Zhang MJ,Bagaria VK,Zou Jdoi
10.1038/s41467-018-04608-8subject
Has Abstractpub_date
2018-05-30 00:00:00pages
2134issue
1issn
2041-1723pii
10.1038/s41467-018-04608-8journal_volume
9pub_type
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