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

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.

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

Jurnal Geologi dan Sumberdaya Mineral ◽

10.33332/jgsm.geologi.20.3.119-131 ◽

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.

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Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

Remote Sensing ◽

10.3390/rs13020318 ◽

2021 ◽

Vol 13 (2) ◽

pp. 318

Author(s):

Jae-Jin Park ◽

Kyung-Ae Park ◽

Pierre-Yves Foucher ◽

Philippe Deliot ◽

Stephane Le Floch ◽

...

Keyword(s):

Near Infrared ◽

Spectral Characteristics ◽

Chemical Properties ◽

Principal Component ◽

Component Analysis ◽

Noise Removal ◽

Vertex Component Analysis ◽

Shortwave Infrared ◽

Spectral Mixture ◽

Physical And Chemical

With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

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

Plant Genetic Resources ◽

10.1079/pgr200322 ◽

2003 ◽

Vol 1 (2-3) ◽

pp. 151-156 ◽

Cited By ~ 1

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.

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Support vector regression is an improvement for principle component analysis

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/06462 ◽

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.

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

Fluctuation and Noise Letters ◽

10.1142/s0219477518500293 ◽

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.

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

Journal of Control Science and Engineering ◽

10.1155/2017/2697297 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 1

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.

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Object Classification via PCANet and Color Constancy Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.997 ◽

2014 ◽

Vol 635-637 ◽

pp. 997-1000 ◽

Cited By ~ 1

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.

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Targeted principle component analysis: A new motion artifact correction approach for near-infrared spectroscopy

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545813500661 ◽

2014 ◽

Vol 07 (02) ◽

pp. 1350066 ◽

Cited By ~ 40

Author(s):

Meryem A. Yücel ◽

Juliette Selb ◽

Robert J. Cooper ◽

David A. Boas

Keyword(s):

Infrared Spectroscopy ◽

Principle Component Analysis ◽

Near Infrared ◽

Spline Interpolation ◽

Motion Artifact ◽

Component Analysis ◽

Motion Artifacts ◽

Artifact Correction ◽

New Approach ◽

Principle Component

As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemodynamic responses. There are various methods for correction such as principle component analysis (PCA), wavelet-based filtering and spline interpolation. Here, we introduce a new approach to motion artifact correction, targeted principle component analysis (tPCA), which incorporates a PCA filter only on the segments of data identified as motion artifacts. It is expected that this will overcome the issues of filtering desired signals that plagues standard PCA filtering of entire data sets. We compared the new approach with the most effective motion artifact correction algorithms on a set of data acquired simultaneously with a collodion-fixed probe (low motion artifact content) and a standard Velcro probe (high motion artifact content). Our results show that tPCA gives statistically better results in recovering hemodynamic response function (HRF) as compared to wavelet-based filtering and spline interpolation for the Velcro probe. It results in a significant reduction in mean-squared error (MSE) and significant enhancement in Pearson's correlation coefficient to the true HRF. The collodion-fixed fiber probe with no motion correction performed better than the Velcro probe corrected for motion artifacts in terms of MSE and Pearson's correlation coefficient. Thus, if the experimental study permits, the use of a collodion-fixed fiber probe may be desirable. If the use of a collodion-fixed probe is not feasible, then we suggest the use of tPCA in the processing of motion artifact contaminated data.

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

Anesthesiology ◽

10.1097/00000542-200006000-00010 ◽

2000 ◽

Vol 92 (6) ◽

pp. 1545-1552 ◽

Cited By ~ 16

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.

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