Review of robust multivariate statistical methods in high dimension

Filzmoser, Peter; Todorov, Valentin

doi:10.1016/j.aca.2011.03.055

Cited by 60 publications

(24 citation statements)

References 24 publications

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“…To test the robustness of our PCA study, we performed three different methods implemented in the program R, 64,65 namely projection pursuit PCA, robust PCA (ROBPCA) and spherical principal components procedure. 66 Optimal number of principal component was determined using the leave-one-out cross-validation for the 0 th and the 2 nd derivatized ATR spectra. 67 Samples of extra virgin olive oils were also analyzed with Euclidean distance cluster analysis (Ward's method) in the program R.…”

Section: Multivariate Data Analysismentioning

confidence: 99%

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

et al. 2013

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Abstract. Extra virgin olive oil samples were collected from different geographical areas of Central Dalmatia region in Croatia including locations both on the coast and islands. This set included 41 oils of cultivar Oblica, 1 per cultivars Coratina and Leccino, and 5 of mixed cultivars Lastovka and Oblica. Attenuated total reflectance (ATR) spectra of non-treated samples were recorded and principal component analysis (PCA) was carried out on a set of obtained spectra, as well as their first and second derivatives. The quality of PCA models was assessed using leave-one-out cross-validation and optimal number of principal components was determined. In the case of ATR spectra, the first principal component accounted for 42.92 % of the total variance among the samples and the optimal number of components was 6, whereas in the case of second derivatives the first principal component accounted for 95.76 % of the total variance and the optimal number of components was 3. Classification of olive oils on the basis of geographical origin was proposed and underlying spectral differences among the spectra were determined by investigating principal component loadings. These differences arise as a result of variations in fatty acid composition. It was found out that ATR in combination with PCA could easily distinguish between samples collected from the coastal area and those from the islands. Classification results were further confirmed by using spherical principal components procedure, projection pursuit and robust PCA, as well as hierarchical cluster analysis.(doi: 10.5562/cca2377)

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Section: Multivariate Data Analysismentioning

confidence: 99%

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

et al. 2013

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“…Over the past several years a number of book chapters, tutorials and review papers have been published, for example [61][62][63][64][65][66][67][68][69]. Over the past several years a number of book chapters, tutorials and review papers have been published, for example [61][62][63][64][65][66][67][68][69].…”

Section: Further Reading and Softwarementioning

confidence: 99%

Robust Methods in Analysis of Multivariate Food Chemistry Data

Stanimirova¹,

Daszykowski²,

Walczak³

2013

Data Handling in Science and Technology

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“…Besides, LDA suffers from the presence of outlying measurements (outliers) in the data. Its robust versions are available [4], which are however computationally infeasible for n < p [9]. Sensitivity to outliers is also a disadvantage of regularized versions of LDA [13].…”

Section: Introductionmentioning

confidence: 99%

Highly robust analysis of keystroke dynamics measurements

Kalina

Schlenker

Kutílek

2015

2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Standard classification procedures of both data min ing and multivariate statistics are sensitive to the presence of outlying values. In this paper, we propose new algorithms for computing regularized versions of linear discriminant analysis for data with small sample sizes in each group. Further, we propose a highly robust version of a regularized linear discriminant analysis. The new method denoted as MWCD-L2-LDA is based on the idea of implicit weights assigned to individual observations , inspired by the minimum weighted covariance determinant esti mator. Classification performance of the new method is illustrated on a detailed analysis of our pilot study of authentication methods on computers , using individual typing characteristics by means of keystroke dynamics.

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Review of robust multivariate statistical methods in high dimension

Cited by 60 publications

References 24 publications

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

Chemometric Analysis of Croatian Extra Virgin Olive Oils from Central Dalmatia Region

Robust Methods in Analysis of Multivariate Food Chemistry Data

Highly robust analysis of keystroke dynamics measurements

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