# Principal Component Projection with Low-Degree Polynomials

## TL;DR

This paper introduces a novel method for approximating principal component projection using low-degree polynomials, improving efficiency by replacing rational functions with carefully chosen polynomials.

## Contribution

It develops a new approach that replaces rational functions with optimized low-degree polynomials for PCP approximation, with theoretical and numerical validation.

## Key findings

- Optimal polynomials improve approximation accuracy
- The method reduces computational complexity
- Numerical experiments confirm effectiveness

## Abstract

In this paper, we consider approximations of principal component projection (PCP) without explicitly computing principal components. This problem has been studied in several recent works. The main feature of existing approaches is viewing the PCP matrix as a matrix function. This underlying function is the composition of a step function with a rational function. To find an approximate PCP, the step function is approximated by a polynomial while the rational function is evaluated by a fast ridge regression solver. In this work, we further improve this process by replacing the rational function with carefully constructed polynomials of low degree. We characterize the properties of polynomials that are suitable for approximating PCP, and establish an optimization problem to select the optimal one from those polynomials. We show theoretically and confirm numerically that the resulting approximate PCP approach with optimal polynomials is indeed effective for approximations of principal component projection.