Robust vs. classical principalcomponent analysis in the presence of outliers

Atakan

Journal of Applied Statistics

2015

Principal component analysis (PCA) is a popular technique that is useful for dimensionality reduction but it is affected by the presence of outliers. The outlier sensitivity of classical PCA (CPCA) has caused the development of new approaches. Effects of using estimates obtained by expectation-maximization -EM and multiple imputation -MI instead of outliers were examined on the artificial and a real data set. Furthermore, robust PCA based on minimum covariance determinant (MCD), PCA based on estimates obtained by EM instead of outliers and PCA based on estimates obtained by MI instead of outliers were compared with the results of CPCA. In this study, we tried to show the effects of using estimates obtained by MI and EM instead of outliers, depending on the ratio of outliers in data set. Finally, when the ratio of outliers exceeds 20%, we suggest the use of estimates obtained by MI and EM instead of outliers as an alternative approach.

Section: Robust Pcamentioning

confidence: 99%

A comparison of different procedures for principal component analysis in the presence of outliers

Atakan

Journal of Applied Statistics

2015

Methods in Molecular Biology

“…The main concern is that the outliers may skew the first few mode directions. While there are robust algorithms that are useful in stabilizing PCA in the presence of outliers [25–32], it is often effective to remove identifiable outliers or simply consider a sufficiently long trajectory for which the results are significant. Generating a large number of conformational samples and removal of outliers before the C-matrix is calculated mitigates concerns about robustness of the results.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins

David

Jacobs

2013

824

533

It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided.

Int J Syst Assur Eng Manag

“…Principal component analysis (PCA) is a dimensionality reduction approach. It converts the multidimensional data into a lower dimension for more straightforward analysis (Sapra 2010 ). Traditional PCA is sensitive to anomalies and can mislead in results in the existence of anomalies.…”

Section: Condition-based Maintenance (Cbm)mentioning

confidence: 99%

Condition-based maintenance using machine learning and role of interpretability: a review

Sharma

Mittal

Soni

2022

This article aims to review the literature on condition-based maintenance (CBM) by analyzing various terms, applications, and challenges. CBM is a maintenance technique that monitors the existing condition of an industrial asset to determine what maintenance needs to be performed. This article enlightens the readers with research in condition-based maintenance using machine learning and artificial intelligence techniques and related literature. A bibliometric analysis is performed on the data collected from the Scopus database. The foundation of a CBM is accurate anomaly detection and diagnosis. Several machine-learning approaches have produced excellent results for anomaly detection and diagnosis. However, due to the black-box nature of the machine learning models, the level of their interpretability is limited. This article provides insight into the existing maintenance strategies, anomaly detection techniques, interpretable models, and model-agnostic methods that are being applied. It has been found that significant work has been done towards condition based-maintenance using machine learning, but explainable artificial intelligence approaches got less attention in CBM. Based on the review, we contend that explainable artificial intelligence can provide unique insights and opportunities for addressing critical difficulties in maintenance leading to more informed decision-making. The analysis put forward encouraging research directions in this area.