Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data

Wu, Wen; Mallet, Y.; Walczak, Beata; Penninckx, W.; Massart, D.L.; Heuerding, S.; Erni, F.

doi:10.1016/0003-2670(96)00142-0

Cited by 214 publications

(109 citation statements)

References 10 publications

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“…Quadratic Discriminant Analysis (QDA) is employed to classify each feature (x) in pre-existing different groups, from the information of a set of variables (x), called variable classifiers [55]. The information of each variable (x) is synthesized in a discriminant function.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Machine Learning for Wind Turbine Blades Maintenance Management

2017

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Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule-Walker model is employed for feature extraction, and Akaike's information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score.

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Section: Machine Learning Approachmentioning

confidence: 99%

Machine Learning for Wind Turbine Blades Maintenance Management

2017

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“…Linear discriminant analysis [27,28] between the between-class variance and the within-class variance.…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Detection of counterfeit Viagra® by Raman microspectroscopy imaging and multivariate analysis

Sacré

Deconinck

Saerens

et al. 2011

Journal of Pharmaceutical and Biomedical Analysis

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During the past years, pharmaceutical counterfeiting was mainly a problem of developing countries with weak enforcement and inspection programs. However, Europe and North America are more and more confronted with the counterfeiting problem. During this study, 26 counterfeits and imitations of Viagra ® tablets and 8 genuine tablets of Viagra ® were analysed by Raman microspectroscopy imaging.After unfolding the data, three maps are combined per sample and a first PCA is realised on these data. Then, the first principal components of each sample are assembled. The exploratory and classification analysis are performed on that matrix.PCA was applied as exploratory analysis tool on different spectral ranges to detect counterfeit medicines based on the full spectra (200-1800 cm After the exploratory analysis, three different classification algorithms were applied on the full spectra dataset: linear discriminant analysis, k-nearest neighbour and soft independent modelling of class analogy.PCA analysis of the 830-880cm -1 spectral region discriminated genuine samples while the multivariate analysis of the spectral region between 1200-1290 cm -1 returns no satisfactory results.A good discrimination of genuine samples was obtained with multivariate analysis of the full spectra region (200-1800 cm -1). Application of the k-NN and SIMCA algorithm returned 100% correct classification during both internal and external validation.

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“…Allocation or discriminant rules are developed from randomly sampled "learning" or "training" data drawn from k known populations, 1 ,..., K π π and based upon the allocation rules, future observations are placed into groups 1 ,..., K ω ω (Johnson and Wichern, 2002;Rencher, 2002;Afifi and Clark?et al, 2004 (Wu et al, 1996;Johnson and Wichern, 2002;Afiti et al, 2004). The observed object i x is assigned to the class k ω which produces the lowest classification score (Wu et al, 1996). The classification score for RDA is given by…”

Section: Discriminant Analysismentioning

confidence: 99%