Exploring patterns enriched in a dataset with contrastive principal component analysis

By Abubakar Abid, Martin Jinye Zhang, Vivek Kumar Bagaria, James Y. Zou. 2018

234 citations recorded by the source corpus.

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. Dimensionality reduction and visualization methods lack a principled way of comparing multiple datasets. Here, Abid et al. introduce contrastive PCA, which identifies low-dimensional structures enriched in one dataset compared to another and enables visualization of dataset-specific patterns.

Subfields: Analytical Chemistry, Molecular Biology, Statistics and Probability, Representation & Self-Supervised Learning

Citations in the corpus

• Overview and comparative study of dimensionality reduction techniques for high dimensional data (2020, unknown): “Abid et al. [55] proposed Contrastive PCA (cPCA) to get low dimensional structures from the given data and find better insights. cPCA has shown suitability for application areas where PCA has already been used.”