Dual‐constrained and primal‐constrained principal component analysis

Andries, Erik; Nikzad‐Langerodi, Ramin

doi:10.1002/cem.3403

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…After the segmentation process, the EEG signals were explored using Multiscale PCA. This is because it looks for the subspaces that maximize the sum of all squared pairwise distances between data projections [11]. Then DWT leader feature extraction method has been used to extract the feature vectors of the EEG signals.…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic Detection of Mild Cognitive Impairment from EEG Recordings Using Discrete Wavelet Transform Leader and Ensemble Learning Methods

SAİD

Göker²

2023

DÜMF MD

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Mild Cognitive Impairment (MCI) is a risk of cognitive decline, commonly referred to as a transitional stage between normal cognition and dementia. Patients with MCI typically progress to Alzheimer's disease (AD), which causes cognitive deficits such as deterioration of their thinking abilities. This study aims to detect MCI patients using electroencephalography (EEG) signals. The EEG dataset used in this study consists of EEG signals recorded from 18 MCI and 16 control groups. Firstly, EEG signals were denoised using multiscale principal component analysis (multiscale PCA). Then, 36 features were extracted from the EEG signals using the discrete wavelet transform leader (DWT leader) feature extraction method. Finally, using the extracted feature vectors, control groups, and MCI groups were classified by ensemble learning algorithms. As a result, AdaBoostM1 algorithm has the highest success with 93.50% accuracy, 93.27% sensitivity, 93.75% specificity, 94.38% precision, 93.82% f1-score, and 86.97% Matthews correlation coefficient (MCC). By achieving quite satisfactory accuracy, this study proves that the ensemble learning algorithm can also be used for MCI detection.

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Section: Proposed Methodsmentioning

confidence: 99%

Automatic Detection of Mild Cognitive Impairment from EEG Recordings Using Discrete Wavelet Transform Leader and Ensemble Learning Methods

SAİD

Göker²

2023

DÜMF MD

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“…All of these methods rely on distances that use all the measured microbiome count without incorporating individual covariances, i.e., the importance of individual microbial species is lost. Emerging approaches (Satten et al, 2017;Andries and Nikzad-Langerodi, 2022) do offer a bit of reprieve, but a concerted effort is required to develop this direction. 2.…”

Section: Grand Challenge: How Can We Unravel the Mediating Role Of Mi...mentioning

confidence: 99%

Specialty grand challenge: how can we use integrative approaches to understand microbial community dynamics?

Ijaz,

Ameer,

Saleem

et al. 2024

Front. Syst. Biol.

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“…Making the variables similar is the goal. The variables are often normalized to have a mean of zero, a standard deviation of one, and a standard deviation of one [20,21].…”

Section: Data Standardizationmentioning

confidence: 99%

Application of Multidimensional Statistical Methods to the Hydrochemical Study with R Software

Chaal¹,

Hamdane²,

Aboutafail³

2022

MMEP

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Conducting extensive, time-consuming analysis campaigns is a typical technique to better understand and manage surface water quality. These usually generate a substantial amount of data that is challenging to comprehend. Principal component analysis may be advantageous for such a project (PCA). From the perspective of such an application, eight physico-chemical parameters are important: Sodium (Na+), Bicarbonate (HCO3-), Magnesium (Mg2+), Total Alkalinity (as CaCO3), Chlorides (Cl-), Potassium (K+), Calcium (Ca2+), Sulfates (SO42-), coming from the analysis of 100 water samples collected between February 2014 and December 2015 on 25 stations distributed on Inaouen catchment areas, were analyzed. The principal component analysis applied to the data showed that the variables could be grouped into two principal components. The interpretation of the results using these tools allowed us to understand that the parameters responsible for water quality are related to component Dim1 (HCO3-, CaCO3, K+, Cl-, Na+ and SO42-) and component Dim2 to the processes associated to (Ca2+ and Mg2+) for the physicochemical parameters, the Dim1 factorial design accounts for 67.80% of the variance; it is expressed towards its positive pole by HCO3-, CaCO3, K+, Cl-, Na+ and SO42-, which present good correlations between them. However, the Dim2 factorial plane represents only 17.60%, defined by the Ca2+ and Mg2+ ions towards its positive pole. The Dim1XDim2 plane's typological structure reveals the individualization of three different groupings based on their hydrochemical quality. A feasible reduction in the number of dimensions without a major loss of information was discovered by the PCA. This tool is a good choice from the standpoint of developing management tools.

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Dual‐constrained and primal‐constrained principal component analysis

Cited by 4 publications

References 31 publications

Automatic Detection of Mild Cognitive Impairment from EEG Recordings Using Discrete Wavelet Transform Leader and Ensemble Learning Methods

Automatic Detection of Mild Cognitive Impairment from EEG Recordings Using Discrete Wavelet Transform Leader and Ensemble Learning Methods

Specialty grand challenge: how can we use integrative approaches to understand microbial community dynamics?

Application of Multidimensional Statistical Methods to the Hydrochemical Study with R Software

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