Singular Value Decomposition

Caltenco, J. H.; López‐Bonilla, J.; Carvajal-Gámez, Blanca E.; Lam-Estrada, P.

doi:10.18052/www.scipress.com/bsmass.11.13

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…Note that simpler predictive models with fewer input variables may perform better when generating predictions based on new data [ 41 ]. For example, a matrix’s Singular Value Decomposition (SVD) is a factorization of linear algebra into three different matrices and transforms a dataset from its original dimension form into a new compressed dimension [ 42 ], as shown in Figure 5 and Equation (6).…”

Section: Methodsmentioning

confidence: 99%

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

et al. 2022

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Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.

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Section: Methodsmentioning

confidence: 99%

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

et al. 2022

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“…e singular value decomposition (SVD) can improve PCA [24,25]. Hence, we will utilize the SVD-based PCA to examine the contributions of the 12 environmental variables to FX, where SVD is used to calculate the eigenvalue and eigenvector of the covariance matrix [25]. For the statement convenience, we symbolize the proposed model as SVD-PCA-ANN throughout the paper.…”

Section: E Proposed Modelmentioning

confidence: 99%

“…We will reconstruct k-dimensional features based on the original n-dimensional features and then map the n-dimensional features to k-dimensional features (known as the main components) [19,21,25]. e output of the SVD-based PCA will be input into ANN-a multilayer forward back propagation network with 2 input layers, 4 hidden layers, and 1 output layer.…”

Section: E Proposed Modelmentioning

confidence: 99%

Examining the Potential Environmental Controls of Underground CO2 Concentration in Arid Regions by an SVD-PCA-ANN Preview Model

Zhuang

Li²,

Wang

et al. 2021

Mathematical Problems in Engineering

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This study attempts to examine environmental controls of the underground CO2 concentration, taking the CO2 concentration 4 m beneath the soil as an example. An SVD-PCA-ANN (singular value decomposition-principal component analysis-artificial neural network) preview model is proposed with the data of underground CO2 concentration and 12 environmental variables (the soil and meteorological data). The R2, RMSE, and RPD values of the proposed model are, respectively, 0.8874, 0.3351, and 2.7929, performing better than the popular preview models like SAE (stacked autoencoders), SVM (support vector machine), and LSTM (long short-term memory). It is proved that the underground CO2 concentration can be approximated by a nonlinear function of the considered variables. Soil temperature, salinity, and wind speed are the leading environmental controls, which explain 32.04%, 13.68%, and 11.21% in the variability of the underground CO2 concentration, respectively. Possible mechanisms associated with the environmental controls are also preliminarily discussed.

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“…Due to the high variability of the shape and frequency composi�on of the components that are inter-es�ng to us, it may be necessary to analyze signal on different types of wavelets, or use complex mathema�cal func�ons, which can complicate the calcula�on. Also, methods of linear matrix decomposi�on are used [12] to reduce the amount of noise and irregular inclusions in the signal, as well as to reduce the atenua-�on effect of regular low-amplitude components (due to the variability of RR and other ECG intervals). These methods make it possible to use a smaller number of cardiocycles by extrac�ng noise disturbances in vectors with smaller singular values than in the useful signal.…”

Section: Introductionmentioning

confidence: 99%

The Method of Preprocessing of ECG Signals for Detection of Atrial and Ventricular Late Potentials

Mnevets

Ivanushkina

2023

Microsyst., Electron. & Acoust.

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This article is aimed at analyzing and improving the methods of preprocessing ECG signals for the task of detecting low-amplitude regular components. This study analyzed the main advantages and disadvantages of existing ECG signal preprocessing methods for the detection of late ventricular and atrial potentials. Based on this analysis, a cardiac cycle averaging method was proposed in order to increase the accuracy of detection of late potentials by various algorithms and improve the quality of preprocessing of the ECG signal aimed at detection of low-amplitude components. The main feature of the proposed method is the division of a large number of cardiocycles for averaging into smaller aggregates (epochs), and the subsequent application of linear matrix decomposition to suppress irregular inclusions. Also, when dividing into epochs, it can be used overlapping. It can reduce the difference between epochs, and increase the number of cardiocycles for averaging. The use of this approach allows to minimize irregular inclusions in the ECG signal and increase the accuracy of the selection of low-amplitude late potentials. In addition, the division into epochs and overlapping makes possible to avoid blurring of low-amplitude high-frequency components during averaging as a result of heart rate variability, as well as to improve the quality of averaging with a reduced number of cardiocycles. To test the proposed method, various approaches were used to assess the ECG signal preprocessing. Mostly, we compared the cardiac cycles obtained as a result of different averaging algorithms and the proposed method with the template. To test the averaging method, an artificial ECG signal was developed with existing noise, late ventricular and atrial potentials, heart rate variability, and a high-amplitude component that occurs at a random location every two heartbeats. The template cardiac cycle was obtained from the original artificial signal without any distortion or noise. Firstly, we visually compared and evaluated different averaging methods with the template. Secondly, we calculated the similarity metrics of the late potentials on the averaged cardiac cycle with the late potentials on the template signal. Based on these metrics, the curves of dependence of the similarity values on the amplitude of late potentials on the ECG signal were calculated. Thirdly, we evaluated the impact of the proposed averaging method on the classification results of various machine learning algorithms on real ECG signals with available late potentials. The overall testing result showed that the proposed averaging method is able to reproduce the morphology of low-amplitude regular components by 10-30% more accurately and improve the classification accuracy by 5-12%.

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Singular Value Decomposition

Cited by 3 publications

References 22 publications

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Examining the Potential Environmental Controls of Underground CO2 Concentration in Arid Regions by an SVD-PCA-ANN Preview Model

The Method of Preprocessing of ECG Signals for Detection of Atrial and Ventricular Late Potentials

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