# All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance

## TL;DR

This paper examines the computational challenges of sparse PCA, demonstrating that traditional PCA methods for scores and residuals are inadequate, and provides corrected computation methods through theoretical and numerical analysis.

## Contribution

It highlights the non-orthogonality issue in sPCA and offers proper computation techniques for scores, residuals, and explained variance.

## Key findings

- Traditional PCA methods are not suitable for sPCA.
- sPCA components may not be orthogonal, affecting interpretation.
- State-of-the-art sPCA algorithms show limited performance on noise-free data.

## Abstract

Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity with the ultimate goal of improving data interpretation. When moving from PCA to sPCA, there are a number of implications that the practitioner needs to be aware of. A relevant one is that scores and loadings in sPCA may not be orthogonal. For this reason, the traditional way of computing scores, residuals and variance explained that is used in the classical PCA cannot directly be applied to sPCA models. This also affects how sPCA components should be visualized. In this paper we illustrate this problem both theoretically and numerically using simulations for several state-of-the-art sPCA algorithms, and provide proper computation of the different elements mentioned. We show that sPCA approaches present disparate and limited performance when modeling noise-free, sparse data. In a follow-up paper, we discuss the theoretical properties that lead to this problem.