@inproceedings{ab09079b0fcb4fd8a4cd50a04b658457,
title = "Mean squared error vs. frame potential for unsupervised variable selection",
abstract = "Forward Selection Component Analysis (FSCA) provides a pragmatic solution to the NP-hard unsupervised variable selection problem, but is not guaranteed to be optimal due to the multi-modal nature of the mean squared error (MSE) selection metric used. Frame potential (FP) is a metric that has recently been shown to yield near-optimal greedy sensor selection performance for linear inverse problems. This paper explores if FP offers similar benefits in the unsupervised variable selection context. In addition, the backward elimination counterpart of FSCA is introduced for the first time (BECA) and compared with forward and backward FP based variable selection on a number of simulated and real world datasets. It is concluded that FP does not improve on FSCA and that while BECA yields comparable results to FSCA it is not a competitive alternative due to its much higher computational complexity.",
keywords = "Frame potential, Greedy algorithm, Unsupervised dimensionality reduction, Variable selection",
author = "Federico Zocco and Se{\'a}n McLoone",
year = "2017",
month = aug,
day = "23",
doi = "10.1007/978-981-10-6373-2_36",
language = "English",
isbn = "9789811063725",
volume = "762",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "353--362",
booktitle = "Intelligent Computing, Networked Control, and Their Engineering Applications - International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Proceedings",
address = "Germany",
note = "International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, ICSEE 2017 ; Conference date: 22-09-2017 Through 24-09-2017",
}