[图书][B] Robust methods for data reduction
A Farcomeni, L Greco - 2016 - books.google.com
This book gives a non-technical overview of robust data reduction techniques, encouraging
the use of these important and useful methods in practical applications. The main areas …
the use of these important and useful methods in practical applications. The main areas …
Uncertainty and error
A Evans - Agent-based models of geographical systems, 2011 - Springer
Errors in input data, parameterisation, and model form cause errors and uncertainty in model
outputs. This is particularly problematic in non-linear systems where small changes …
outputs. This is particularly problematic in non-linear systems where small changes …
The forward search: Theory and data analysis
Abstract The Forward Search is a powerful general method, incorporating flexible data-
driven trimming, for the detection of outliers and unsuspected structure in data and so for …
driven trimming, for the detection of outliers and unsuspected structure in data and so for …
The power of monitoring: how to make the most of a contaminated multivariate sample
Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in
the presence of contamination by outliers. However, a disadvantage of having a single, even …
the presence of contamination by outliers. However, a disadvantage of having a single, even …
A multi-step anomaly detection strategy based on robust distances for the steel industry
Steel making industries exhibit extreme working conditions characterized by high
temperature, pressure, and production speed as well as intense throughput. Due to high …
temperature, pressure, and production speed as well as intense throughput. Due to high …
Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning
L Cerdán, D Roca-Sanjuán - Journal of Chemical Theory and …, 2022 - ACS Publications
The theoretical prediction of molecular electronic spectra by means of quantum mechanical
(QM) computations is fundamental to gain a deep insight into many photophysical and …
(QM) computations is fundamental to gain a deep insight into many photophysical and …
The minimum weighted covariance determinant estimator for high-dimensional data
J Kalina, J Tichavský - Advances in Data Analysis and Classification, 2022 - Springer
In a variety of diverse applications, it is very desirable to perform a robust analysis of high-
dimensional measurements without being harmed by the presence of a possibly larger …
dimensional measurements without being harmed by the presence of a possibly larger …
A reweighting approach to robust clustering
An iteratively reweighted approach for robust clustering is presented in this work. The
method is initialized with a very robust clustering partition based on an high trimming level …
method is initialized with a very robust clustering partition based on an high trimming level …
Comparison of local outlier detection techniques in spatial multivariate data
M Ernst, G Haesbroeck - Data mining and knowledge discovery, 2017 - Springer
Outlier detection techniques in spatial data should allow to identify two types of outliers:
global and local ones. Local outliers typically have non-spatial attributes that strongly differ …
global and local ones. Local outliers typically have non-spatial attributes that strongly differ …
ICS for multivariate outlier detection with application to quality control
In high reliability standards fields such as automotive, avionics or aerospace, the detection
of anomalies is crucial. An efficient methodology for automatically detecting multivariate …
of anomalies is crucial. An efficient methodology for automatically detecting multivariate …