Online citations, reference lists, and bibliographies.
← Back to Search

Utility Metrics For Assessment And Subset Selection Of Input Variables For Linear Estimation [Tips & Tricks]

A. Bertrand
Published 2018 · Computer Science

Save to my Library
Download PDF
Analyze on Scholarcy
Share
This tutorial article introduces the utility metric and its generalizations, which allow for a quick-and-dirty quantitative assessment of the relative importance of the different input variables in a linear estimation model. In particular, we show how these metrics can be cheaply calculated, thereby making them very attractive for model interpretation, online signal quality assessment, or greedy variable selection. The main goal of this article is to provide a transparent and consistent framework that consolidates, unifies, and extends the existing results in this area. In particular, we 1) introduce the basic utility metric and show how it can be calculated at virtually no cost, 2) generalize it toward group-utility and noise-impact metrics, and 3) further extend it to cope with linearly dependent inputs and minimum norm requirements.
This paper references
10.1155/2017/3173196
Adaptive Quantization for Multichannel Wiener Filter-Based Speech Enhancement in Wireless Acoustic Sensor Networks
F. H. Arce (2017)
The matrix cookbook . [ Online ] Generalized inverses of partitioned matrices
K. B. Petersen (2012)
10.1109/TASLP.2017.2786544
Microphone Subset Selection for MVDR Beamformer Based Noise Reduction
J. Zhang (2018)
10.1198/tech.2007.s518
Pattern Recognition and Machine Learning
R. Neal (2007)
10.1137/S0895479898332928
On the Optimality of the Backward Greedy Algorithm for the Subset Selection Problem
C. Couvreur (2000)
10.1137/0113070
Generalized Inverses of Partitioned Matrices
C. Rohde (1965)
10.1109/TSP.2012.2210888
Efficient Calculation of Sensor Utility and Sensor Removal in Wireless Sensor Networks for Adaptive Signal Estimation and Beamforming
A. Bertrand (2012)
10.1109/78.476428
Sparse LCMV beamformer design for suppression of ground clutter in airborne radar
I. Scott (1995)
The Matrix Cookbook
K. B. Petersen (2006)
10.5281/ZENODO.42220
Efficient sensor subset selection and link failure response for linear MMSE signal estimation in wireless sensor networks
A. Bertrand (2010)
10.1111/J.1467-9868.2011.00771.X
Regression shrinkage and selection via the lasso: a retrospective
R. Tibshirani (2011)
10.1111/J.1467-9868.2005.00532.X
Model selection and estimation in regression with grouped variables
M. Yuan (2006)
10.1111/J.1467-9868.2005.00503.X
Regularization and variable selection via the elastic net
H. Zou (2005)
10.1109/LSP.2016.2591720
Generalized Signal Utility for LMMSE Signal Estimation With Application to Greedy Quantization in Wireless Sensor Networks
F. H. Arce (2016)
10.1109/SPCOM.2014.6983912
Sensor selection for estimation, filtering, and detection
Sundeep Prabhakar Chepuri (2014)
10.1016/j.sigpro.2013.06.010
Greedy distributed node selection for node-specific signal estimation in wireless sensor networks
Joseph Szurley (2014)
Understanding deep learning requires rethinking generalization
C. Zhang (2017)



This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar