DeepAI AI Chat
Log In Sign Up

A note on the prediction error of principal component regression

11/07/2018
by   Martin Wahl, et al.
Humboldt-Universität zu Berlin
0

We analyse the prediction error of principal component regression (PCR) and prove non-asymptotic upper bounds for the corresponding squared risk. Under mild assumptions, we conclude that PCR performs as well as the oracle method obtained by replacing empirical principal components by their population counterparts. Our approach relies on perturbation bounds for the excess risk of principal component analysis.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/09/2022

A note on the prediction error of principal component regression in high dimensions

We analyze the prediction error of principal component regression (PCR) ...
07/19/2021

Van Trees inequality, group equivariance, and estimation of principal subspaces

We establish non-asymptotic lower bounds for the estimation of principal...
05/15/2014

Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent

We propose a new two stage algorithm LING for large scale regression pro...
03/07/2018

Sketching for Principal Component Regression

Principal component regression (PCR) is a useful method for regularizing...
10/03/2022

Hip Fracture Prediction using the First Principal Component Derived from FEA-Computed Fracture Loads

Hip fracture risk assessment is an important but challenging task. Quant...
10/27/2017

Quantifying the Estimation Error of Principal Components

Principal component analysis is an important pattern recognition and dim...
06/26/2019

Principal Component Analysis for Multivariate Extremes

The first order behavior of multivariate heavy-tailed random vectors abo...