Principle Component Analysis Based on New Symmetric Similarity Measures for Heavy-Tailed Data

2018 ◽  
Vol 17 (04) ◽  
pp. 1850029
Author(s):  
Mohammad Seidpisheh ◽  
Adel Mohammadpour
Keyword(s):  
Principal Component Analysis ◽  
Similarity Measure ◽  
Principle Component Analysis ◽  
Similarity Measures ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Artificial Datasets

We consider the principal component analysis (PCA) for the heavy-tailed distributions. A traditional measure for the classical PCA is the covariance measure. Due to the non-existence of variance of many heavy-tailed distributions, this measure cannot be used for them. We will clarify how to perform PCA in heavy-tailed data by extending a similarity measure based on covariance. We introduce similarity measures based on a new dependence coefficient of heavy-tailed distributions. Using real and artificial datasets, the performance of the proposed PCA is evaluated and compared with the classical one.

A strategy for selecting diverse accessions using Principal Component Analysis from a large germplasm collection of soybean

2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principle Component Analysis ◽  
Quantitative Traits ◽  
Germplasm Collection ◽  
Principal Component ◽  
Target Population ◽  
Component Analysis ◽  
Principle Component ◽  
The Individual

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.

scholarly journals Support vector regression is an improvement for principle component analysis

2021 ◽  
Vol 23 (06) ◽  
pp. 1699-1715
Author(s):  
Mohamed, A. M. ◽  
◽  
Abdel Latif, S. H ◽  
Alwan, A. S. ◽  
◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Principle Component Analysis ◽  
Mean Squared Error ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Functions ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Principle Component

The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size.

Object Classification via PCANet and Color Constancy Model

2014 ◽  
Vol 635-637 ◽  
pp. 997-1000 ◽  
Author(s):  
De Kun Hu ◽  
Li Zhang ◽  
Wei Dong Zhao ◽  
Tao Yan
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Functional Properties ◽  
Principle Component Analysis ◽  
Color Constancy ◽  
Principal Component ◽  
Object Classification ◽  
Component Analysis ◽  
Color Images ◽  
Principle Component

In order to classify the objects in nature images, a model with color constancy and principle component analysis network (PCANet) is proposed. The new color constancy model imitates the functional properties of the HVS from the retina to the double-opponent cells in V1. PCANet can be designed and learned extremely, which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms. At last, a SVM is trained to classify the object in the image. The results of experiments demonstrate the potential of the model for object classification in wild color images.

Topography of Clonidine-induced Electroencephalographic Changes Evaluated by Principal Component Analysis

2000 ◽  
Vol 92 (6) ◽  
pp. 1545-1552 ◽  
Author(s):  
Petra Bischoff ◽  
Eckehard Scharein ◽  
Gunter N. Schmidt ◽  
Georg von Knobelsdorff ◽  
Burkhart Bromm ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Frequency Band ◽  
Principle Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fast Fourier Transformation ◽  
Complex Data ◽  
Multivariate Statistical ◽  
Double Blind ◽  
Principle Component

Background Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. Methods Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 leads and quantified by standard fast Fourier transformation in the delta, theta, alpha, and beta bands. Principle component analysis derived from a correlation matrix calculated between all electroencephalographic leads (26 x 26 leads) separately within each classic frequency band. The basic application level of principle component analysis resulted in components representing clusters of electrodes positions that were differently affected by clonidine. Subjective criteria of drowsiness and anxiety were rated by visual analog scales. Results Topography of clonidine-induced electroencephalographic changes could be attributed to two independent spatial components in each classic frequency band, explaining at least 85% of total variance. The most prominent effects of clonidine were increases in the delta band over centroparietooiccipital areas and decreases in the alpha band over parietooccipital regions. Clonidine administration resulted in subjective drowsiness. Conclusions Data from the current study supported the fact that spatial principle component analysis is a useful multivariate statistical procedure to evaluate significant signal changes from multichannel electroencephalographic recordings and to describe the topography of the effects. The clonidine-related changes seen here were most probably results of its sedative effects.

scholarly journals GC-MS fingerprints of mint essential oils

2015 ◽  
Vol 13 (1) ◽  
Author(s):  
Mirosław A. Hawrył ◽  
Krystyna Skalicka-Woźniak ◽  
Ryszard Świeboda ◽  
Małgorzata Niemiec ◽  
Klaudia Stępak ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Essential Oils ◽  
Secondary Metabolite ◽  
Similarity Measures ◽  
Principal Component ◽  
Component Analysis ◽  
Metabolite Content

AbstractEssential oils from eleven Mentha species were obtained by Deryng hydrodistillation and analysed by GC–MS: 44 compounds were identified. The most abundant were menthone, isomenthone, menthol, carvone, piperitone oxide, D-limonene and eucalyptol. Chemometric similarity measures and principal component analysis were calculated, allowing comparisons based on secondary metabolite content. The fingerprints may be helpful in chemotaxonomy.

scholarly journals Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoming Xu ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Principal Components ◽  
Information Entropy ◽  
Principle Component Analysis ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Original Dataset ◽  
Diagnosis Method

In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

scholarly journals Data Analysis and Data Classification in Machine Learning using Linear Regression and Principal Component Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 835-844
Author(s):  
Lokasree B S
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Principal Component Analysis ◽  
Linear Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Error Reduction ◽  
Principle Component ◽  
Regression Methods ◽  
Step Procedure

In this paper step-by-step procedure to implement linear regression and principal component analysis by considering two examples for each model is explained, to predict the continuous values of target variables. Basically linear regression methods are widely used in prediction, forecasting and error reduction. And principle component analysis is applied for facial recognition, computer vision etc. In Principal component analysis, it is explained how to select a point with respect to variance. And also Lagrange multiplier is used to maximize the principle component function, so that optimized solution is obtained

scholarly journals Aplikasi Principle Component Analysis dan Directed Principal Component untuk Pemetaan Alterasi Hidrotermal menggunakan Citra ASTER di Kecamatan Kokap, Kulon Progo

2019 ◽  
Vol 20 (3) ◽  
pp. 119
Author(s):  
Bayu Raharja ◽  
Agung Setianto ◽  
Anastasia Dewi Titisari
Keyword(s):  
Principle Component Analysis ◽  
Near Infrared ◽  
Confusion Matrix ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Shortwave Infrared

Endapan emas di daerah Kokap, Kabupaten Kulon Progo yang berasosiasi dengan endapan tipe epitermal terbentuk akibat adanya proses alterasi hidrotermal. Pemanfaatan data penginderaan jauh untuk pemetaan alterasi telah berhasil dilakukan diberbagai lokasi. Penelitian ini bertujuan untuk mengkaji kemampuan citra ASTER saluran visible near infrared (VNIR) dan saluran shortwave infrared (SWIR) dalam memetakan jenis alterasi menggunakan metode Principle Component Analysis (PCA) dan Directed Principal Component (DPC). Klasifikasi multispektral selanjutnya dilakukan untuk membedakan tipe alterasi hidrotermal menggunakan kombinasi saluran hasil PCA dan DPC. Keberhasilan pemetaan alterasi hidrotermal dievaluasi secara statistik menggunakan confusion matrix. Tingkat akurasi yang dapat diterima adalah sebesar 85% dengan koefisien kappa lebih besar dari 0.8. Hasil penelitian menunjukkan metode DPC memiliki akurasi lebih baik dari PCA dalam memetakan tipe alterasi meskipun tingkat akurasi keduanya dibawah batas yang ditentukan. Saluran ASTER yang paling baik digunakan untuk memetakan tipe alterasi hidrotermal di lokasi penelitian adalah 1, 2, 4, dan 6.

scholarly journals Data Analytics for Cardiotocography Data Using Principal Component Analysis

2018 ◽  
Vol 7 (2.32) ◽  
pp. 233
Author(s):  
Pratuisha K ◽  
Rajeswara Rao .D ◽  
J V.R.Murthy
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Data Analytics ◽  
Early Stage ◽  
Principal Component ◽  
Component Analysis ◽  
Chi Square ◽  
Principle Component ◽  
Precise Diagnosis ◽  
Chi Square Test

With growing congenital anamelies in recent years detection of heart problems in fetus has become critical. Cardiotocography test assists doctors in such dignosis followed by cure. Here analytics of cardiotocography data is presented in details.Understanding ,cleaning and preprocessing the data is one of the the foremost part for any researcher,In this work data is cleaned,preprocessed,normalized, Also the attributes are selected by using the Chi-square test. Colinearity problem is addressed using Principle component analysis.Such analytics and prepro-cessing will help in machine learning or allied models for predict-ing precise diagnosis at an early stage 

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