Year: 2020
Type: article
Abstract: Data pre-processing and exploratory data analysis are crucial steps in the data science lifecycle, often relying on dimensionality reduction techniques to extract pertinent signal.As the collection of large and complex datasets becomes the norm, the need for methods that can successfully glean pertinent information from among increasingly intricate technical artifacts is greater than ever.What's more, many of the most historically reliable and commonly used methods have demonstrably poor performance, or even fail outright, in reducing the dimensionality of large and noisy datasets in a stable, interpretable, and relevant manner.Principal component analysis (PCA) is one such method.Although popular for its interpretable results and ease of implementation, PCA's performance on high-dimensional data often leaves much to be desired.Its performance has been characterized as unstable in such settings (Johnstone & Lu, 2009), and it has been shown to often emphasize unwanted variation (e.g., batch effects) in lieu of the signal of interest.Consequently, modifications of PCA have been developed to remedy these issues.Namely, sparse PCA (SPCA) (Zou, Hastie, & Tibshirani, 2006) was created to increase the stability and interpretability of the principal component loadings in high dimensions, while constrastive PCA (cPCA) (Abid, Zhang, Bagaria, & Zou, 2018) leverages control data to adjust for unwanted effects and capture relevant information.
DOI: 10.21105/joss.02079
Authors: Philippe Boileau, Nima Hejazi, Sandrine Dudoit
Institution: University of California, Berkeley
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3008639173