Exact methods for variable selection in principal component analysis: Guide functions and pre-selection

Pacheco, Joaquín; Casado, Silvia; Porras, Santiago

doi:10.1016/j.csda.2012.06.014

Cited by 33 publications

(22 citation statements)

References 37 publications

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“…Since the hydrogen sensor faced the problem of small signal detection, moving window is better at small variations. Thus, MWPCA can more easily find the principal components which contain abnormal information ignored by PCA and KPCA [25]- [28].…”

Section: The Rapid and Quantitative Hydrogen Concentrationmentioning

confidence: 99%

A Fault Diagnosis and Reconfiguration Strategy for Self-Validating Hydrogen Sensor Array Based on MWPCA and ELM

Song

Chen

et al. 2019

IEEE Access

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The flammable and explosive property of hydrogen is the main danger in its safe use, storage and transportation. In this paper, a novel hydrogen monitoring system is designed based on the principle of semiconductor, catalytic combustion and heat-conducting gas sensors. Also, the gas sensor will inevitably fail due to the nature of gas sensitive materials in the long-time monitoring process. To ensure the accuracy and reliability of hydrogen concentration measurement, a novel fault diagnosis and reconfiguration strategy for hydrogen sensor array based on moving window principle component analysis and extreme learning machine (MWPCA-ELM) is proposed. Firstly, online multiple faults detection is carried out by using MWPCA. Once one or multiple faults are detected, the measured values of other fault-free sensors will be used to recover the faulty data in real-time by using ELM predictor according to the relevancy among the hydrogen sensors. Secondly, the hydrogen concentration is reconfigured seamlessly and accurately based on ELM under the condition of small calibration data sample. Finally, fault diagnosis is conducted by MWPCA feature extraction coupled with ELM multi-classifier. In order to illustrate the effectiveness and feasibility of the proposed fault diagnosis and reconfiguration strategy, a hydrogen concentration monitoring experimental system was established. The average relative error (ARE) of hydrogen concentration estimation is declined from 1.18% to 0.82% compared with the traditional regression methods. Particularly, the proposed fault reconfiguration model can recover the fault data even if the concentration is changed, and the accuracy of fault diagnosis is 100% within 250 samples. INDEX TERMS Fault diagnosis, reconfiguration, hydrogen sensor, extreme learning machine, moving windows principle component analysis.

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Section: The Rapid and Quantitative Hydrogen Concentrationmentioning

confidence: 99%

A Fault Diagnosis and Reconfiguration Strategy for Self-Validating Hydrogen Sensor Array Based on MWPCA and ELM

Song

Chen

et al. 2019

IEEE Access

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“…So far, there have been a variety of linear dimension reduction methods, such as PCA (Principal Component Analysis) [1], MDS (Multidimensional Scaling) [2], LPP (Locality Preserving Projections) [3]. With the nonlinear features of large data, manifold learning has increasingly attracted much attention for its good effect on nonlinear dimension reduction.…”

Section: Introductionmentioning

confidence: 99%

An algorithm research of supervised LLE based on mahalanobis distance and extreme learning machine

Ling

Jin

Yang

et al. 2013

2013 3rd International Conference on Consumer Electronics, Communications and Networks

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The Locally Linear Embedding (LLE) is one of the efficient nonlinear dimensionality reduction techniques. But for some high dimensional data, it is not taking the class information of the data into account and Euclidean distance can not accurately reflect the similarity among samples. The paper proposes an improved Supervised LLE which combines class labeled data and Mahalanobis Distance (MSP-LLE). First, the approach learns a Mahalanobis Distance from the existing data. Then the Mahalanobis Distance and label information are combined to choose neighborhoods. Finally, ELM is using to map the unlabeled data to the feature space, which easily implement fault pattern recognition. The experiment result shows its good performance on reduction and recognition for high-dimensional and similar data.

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“…An important statistical tool capable of simultaneously evaluating more than one measure in individuals or objects under investigation is the Principal Component Analysis (PCA) multivariate technique. This analysis allows the description of a large number of original variables from a smaller number of hypothetical variables (principal components), without significant loss of the original information (Pacheco, Casado, & Porras, 2013;Hair, Black, Babin, & Anderson, 2010).…”

Section: Introductionmentioning

confidence: 99%

Effect of the diet on performance characteristics and quality of meat fat of lamb by principal component analysis

Wanderley

Santos

Lima

et al. 2021

Acta Sci. Anim. Sci.

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The objective of this study was to evaluate the effect of use of agroindustrial co-products in the diet of lambs on the performance and nutritional quality of meat fat. Twenty lambs were distributed into four experimental groups: control diet (C) or diets containing cottonseed (CS), sunflower meal (SFM), and castor cake (CC). During the experimental period, data on dry matter consumption (DMC) and total weight gain (TWG) were collected. After slaughter, samples of Logissimus dorsi (LD) muscle were taken for fatty acids and cholesterol analysis. Diets C and CS provided similar characteristics of fat quality and animal performance. However, SFM and CC diets tend to form isolated groups, with different fat and performance characteristics. The SFM diet results in meat with a higher saturated fatty acids and cholesterol content. The CC diet is related to improved performance characteristics and lower cholesterol content. In conclusion, the inclusion of CC in the experimental diets provided, in the proportions used in this experiment, better characteristics of animal performance and meat fat quality.

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Exact methods for variable selection in principal component analysis: Guide functions and pre-selection

Cited by 33 publications

References 37 publications

A Fault Diagnosis and Reconfiguration Strategy for Self-Validating Hydrogen Sensor Array Based on MWPCA and ELM

A Fault Diagnosis and Reconfiguration Strategy for Self-Validating Hydrogen Sensor Array Based on MWPCA and ELM

An algorithm research of supervised LLE based on mahalanobis distance and extreme learning machine

Effect of the diet on performance characteristics and quality of meat fat of lamb by principal component analysis

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