nonlinear principal component analysis method
The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
... of principal component more effectively from high-dimensional data or a large number of data ...optimization method to deal with linear relation data, but it is not effective to deal with ...
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A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection
... The analysis was performed on NSL-KDD dataset, with and without dimension reduction ...effective method to reduce dimensionality of data by providing a linear transformation of high dimension to low ...
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Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images
... decorrelation method allowing to retain as much information content as possible is ...a method to reduce the spectral dimensionality of HS ...the analysis of the results obtained through the spectral ...
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A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery
... The nonlinear versions of a number of signal process- ing techniques such as principal component analysis (PCA) [13], Fisher discriminant analysis [14], clustering in feature space ...
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CEO Entrenchment and Performance: New Evidence Using Nonlinear Principal Component Analysis
... Following the work of (Bebchuk, Cohen, & Ferrell, 2009; Cai Qian & Liu, 2015; Devos, Elliott, & Warr, 2015; Dikolli, Mayew, and Nanda, 2014; Elyasiani & Zhang, 2015; Florackis & Ozkan 2009; Harjoto, ...
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Fault Detection of The Tennessee Eastman Process Using Improved PCA and Neural Classifier
... Independent Component Analysis (KICA) and SVM to the fault detection and diagnosis in the TEP ...integrating Principal Component Analysis (PCA) with Fuzzy Logic Knowledge-Based (FLKB) ...
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Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis
... extraction method for P300 components using an adaptive nonlinear principal component analy- sis (ANPCA) incorporating the multilayer neural network (MNN) is ...proposed method achieves ...
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Electrocardiogram Beat Classification using Discrete Wavelet Transform, Higher Order Statistics and Multivariate Analysis
... multivariate analysis illustrate the ECG signal more efficiently and achieve good performance under noisy ...multivariate analysis to improve the classification of ECG ...of nonlinear features such ...
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Nonlinear principal component analysis: An alternative method for finding patterns in environmental data.
... non-linear principal components analysis (PCA) provided a gain in explained variation of 10 per cent for the 1st dimension and 6 per cent for the 2nd ...this analysis can be combined with data ...
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Dimensionality Reduction of Image Feature Based on Mean Principal Component Analysis
... and nonlinear methods ...include Principal Component Analysis (PCA) and linear Discriminant Analysis ...the nonlinear dimensionality reduction method, the more ...
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Reliability and validity of the weight efficacy lifestyle questionnaire in overweight and obese individuals
... test-retest method and validity was assessed using different methods such as factor analysis, principal component analysis, items correlation with the total score and with Shrer general ...
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Enforcement of the principal component analysis - extreme learning machine algorithm by linear discriminant analysis
... available method- ologies can be divided in two groups: destructive and constructive ...EM-ELM method can add random basis functions one-by-one or ...
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A 2-Dimensional Gabor-Filters for Face Recognition System: A Survey
... Gabor-filter method proves to be effective technique for facial recognition system, but the high dimensionality of features results into large computational complexity of this ...reduction method which ...
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A Qualitative Analysis Algorithm and Its Application in Mixed Gas Identification
... common method of feature reduction which can change the lots of relevant variables into less independent ...first principal component represents variables as much as possible, the second ...
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A SURVEY: PRINCIPAL COMPONENT ANALYSIS (PCA)
... Principal component analysis (PCA) is one of the most widely used multivariate techniques in ...called principal components. The number of principal components is less than or equal to ...
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Bilinear probabilistic principal component analysis
... Principal component analysis (PCA) [7] is one of the most popular techniques for dimension reduction. While the standard PCA is nonprobabilistic, Moghaddam and Pentland [8] extended it to a ...
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Network Intrusion detection by using PCA via SMO-SVM
... critical component to secure the ...detection method based on Principle Component Analysis (PCA) and SMO-SVM with low overhead and high efficiency is ...proposed method is promising in ...
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Kernel Eigenfaces Framework for Feature Extraction and Face Recognition
... image analysis and ...reliable method of identification because of non-intrusive (without participant’s cooperation) nature of acquisition it is very successful identification and recognition technique ...
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IMPROVED IMAGE DENOISING BASED ON AN HYBRID APPROACH OF WAVELET AND PCA'
... wavelet analysis shows many different ...approach, principal component analysis with local pixel grouping ...a method of identifying patterns in data, and expressing the data in such a ...
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PRINCIPAL COMPONENT ANALYSIS IMAGE DENOISING USING LOCAL PIXEL GROUPING
... In recent years various image processing techniques have been developed. These include medical, satellite, space, transmission and radar etc. But noise in image effect all applications. So it is necessary to remove noise ...
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