Comparisons of a combined wavelet and a combined principal component analysis classification model for BCG signal analysis

2004 ◽  
Author(s):  
Xinsheng Yu ◽  
Dejun Gong ◽  
X. Shuen ◽  
Siren Li ◽  
Yongping Xu
Keyword(s):  
Principal Component Analysis ◽  
Signal Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model

scholarly journals Risk Assessment of Coal Mine Water Inrush Based On PCA-DBN

2021 ◽  
Author(s):  
Zhang ye ◽  
Tang Shoufeng ◽  
Shi Ke
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Coal Mine ◽  
Water Inrush ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Mine Safety ◽  
Training Time

Abstract To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water inbursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China.

scholarly journals A comparative study on dimensionality reduction between principal component analysis and k-means clustering

2019 ◽  
Vol 16 (2) ◽  
pp. 752
Author(s):  
Norsyela Muhammad Noor Mathivanan ◽  
Nor Azura Md.Ghani ◽  
Roziah Mohd Janor
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Time Complexity ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Small Data ◽  
Feature Selection Technique ◽  
Data Set ◽  
Selection Technique

<span>The curse of dimensionality and the empty space phenomenon emerged as a critical problem in text classification. One way of dealing with this problem is applying a feature selection technique before performing a classification model. This technique helps to reduce the time complexity and sometimes increase the classification accuracy. This study introduces a feature selection technique using K-Means clustering to overcome the weaknesses of traditional feature selection technique such as principal component analysis (PCA) that require a lot of time to transform all the inputs data. This proposed technique decides on features to retain based on the significance value of each feature in a cluster. This study found that k-means clustering helps to increase the efficiency of KNN model for a large data set while KNN model without feature selection technique is suitable for a small data set. A comparison between K-Means clustering and PCA as a feature selection technique shows that proposed technique is better than PCA especially in term of computation time. Hence, k-means clustering is found to be helpful in reducing the data dimensionality with less time complexity compared to PCA without affecting the accuracy of KNN model for a high frequency data.</span>

scholarly journals Principal Component Analysis of Phenolic Acid Spectra

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Ronald A. Holser
Keyword(s):  
Principal Component Analysis ◽  
Ir Spectra ◽  
Phenolic Acids ◽  
Phenolic Acid ◽  
Principal Component ◽  
Component Analysis ◽  
Uv Spectra ◽  
Classification Model ◽  
Plant Metabolites ◽  
Spectral Models

Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to develop spectral models for their rapid detection. Results demonstrated that UV and IR spectra could discriminate between each of the phenolic acids in overall models. Calculation of model scores and loadings showed that derivative UV spectra accounted for 99% variation with 2 principal components (PC) while derivative IR spectra required 3 PCs. Individual PCA models were developed for ferulic acid and p-coumaric acid using derivative UV spectra for detection and classification by soft independent modeling of class analogy (SIMCA). The application of this spectral technique as a classification model is expected to promote the use of agricultural residues as a source of these phenolic compounds.

scholarly journals A deep learning framework for characterization of genotype data

2020 ◽  
Author(s):  
Kristiina Ausmees ◽  
Carl Nettelblad
Keyword(s):  
Principal Component Analysis ◽  
Population Structure ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Data Transformation ◽  
Component Analysis ◽  
Classification Model ◽  
Genotype Data

ABSTRACTDimensionality reduction is a data transformation technique widely used in various fields of genomics research, with principal component analysis one of the most frequently employed methods. Application of principal component analysis to genotype data is known to capture genetic similarity between individuals, and is used for visualization of genetic variation, identification of population structure as well as ancestry mapping. However, the method is based on a linear model that is sensitive to characteristics of data such as correlation of single-nucleotide polymorphisms due to linkage disequilibrium, resulting in limitations in its ability to capture complex population structure.Deep learning models are a type of nonlinear machine learning method in which the features used in data transformation are decided by the model in a data-driven manner, rather than by the researcher, and have been shown to present a promising alternative to traditional statistical methods for various applications in omics research. In this paper, we propose a deep learning model based on a convolutional autoencoder architecture for dimensionality reduction of genotype data.Using a highly diverse cohort of human samples, we demonstrate that the model can identify population clusters and provide richer visual information in comparison to principal component analysis, and also yield a more accurate population classification model. We also discuss the use of the methodology for more general characterization of genotype data, showing that models of a similar architecture can be used as a genetic clustering method, comparing results to the ADMIXTURE software frequently used in population genetic studies.

scholarly journals Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant’s Jaundice Symptoms

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Debabrata Samanta ◽  
M. P. Karthikeyan ◽  
Marimuthu Karuppiah ◽  
Dalima Parwani ◽  
Manish Maheshwari ◽  
...  
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Negative Impact ◽  
Feature Selection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Classification Model ◽  
Newborn Jaundice

One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.

High accuracy determination of Angelica dahurica origin based on near infrared spectroscopy and a random forest pruning algorithm

2019 ◽  
Vol 27 (4) ◽  
pp. 278-285 ◽  
Author(s):  
Yonghao Xu ◽  
Li Liu ◽  
Meizhen Huang ◽  
Ning Xu
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Angelica Dahurica ◽  
Pruning Algorithm

A near infrared spectroscopy method combined with a random forest pruning algorithm based on margin optimization and principal component analysis (PCA-MORFP) was proposed to identify the origin of Angelica dahurica. One hundred and ninety-six samples of A. dahurica were collected from four original cultivation regions; their NIR diffuse reflectance spectra were measured by a custom-built near infrared spectrometer which works in the range of 900–1700 nm with a resolution (full width at half maximum [FWHM]) of 4 nm. Combinations of Savitzky–Golay smoothing, standard normal variates, and first derivative transformations were used to preprocess the spectral data. Then the PCA-MORFP classification model was constructed. Meanwhile, the was compared with other classifying approaches, including: principal component analysis-K-nearest neighbor, principal component analysis-support vector machine, and principal component analysis-random forest. Experimental results showed that the PCA-MORFP achieved the best prediction performance over other compared methods. The recognition rates of the PCA-MORFP model were up to 100% for the calibration set and 98.2% for the prediction set, respectively. The method provides a rapid and convenient detection technique for the origin identification of A. dahurica.

scholarly journals Moving objects classification via category-wise two-dimensional principal component analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Falah Alsaqre ◽  
Osama Almathkour
Keyword(s):  
Principal Component Analysis ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Feature Space ◽  
Classification Problem ◽  
Component Analysis ◽  
Classification Model ◽  
Vehicle Classification ◽  
Two Dimensional

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

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