Efficient Robust Mean Value Calculation of 1D Features

01/29/2016
by   Erik Jonsson, et al.
0

A robust mean value is often a good alternative to the standard mean value when dealing with data containing many outliers. An efficient method for samples of one-dimensional features and the truncated quadratic error norm is presented and compared to the method of channel averaging (soft histograms).

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/11/2023

Robust Single Rotation Averaging Revisited

In this work, we propose a novel method for robust single rotation avera...
research
04/21/2020

Robust Motion Averaging under MaximumCorrentropy Criterion

Recently, the motion averaging method has been introduced as an effectiv...
research
06/03/2022

RODIAN: Robustified Median

We propose a robust method for averaging numbers contaminated by a large...
research
10/27/2019

An outlier-robust model averaging approach by Mallows-type criterion

Model averaging is an alternative to model selection for dealing with mo...
research
11/14/2019

Recent Advances in Algorithmic High-Dimensional Robust Statistics

Learning in the presence of outliers is a fundamental problem in statist...
research
02/13/2023

Trimmed sample means for robust uniform mean estimation and regression

It is well-known that trimmed sample means are robust against heavy tail...
research
05/25/2023

Computing the Quadratic Numerical Range

A novel algorithm for the computation of the quadratic numerical range i...

Please sign up or login with your details

Forgot password? Click here to reset