scholarly journals Raman spectrum feature extraction and diagnosis of oil–paper insulation ageing based on kernel principal component analysis

High Voltage ◽  
2020 ◽  
Author(s):  
Dingkun Yang ◽  
Chen Weigen ◽  
Shi Haiyang ◽  
Wan Fu ◽  
Zhou Yongkuo
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Raman Spectrum ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Spectrum Feature ◽  
Paper Insulation

Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-Means Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2390-2394
Author(s):  
Xiao Fang Liu ◽  
Chun Yang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Computational Complexity ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Training Data ◽  
Kernel Principal Component Analysis ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

Nonlinear feature extraction used standard Kernel Principal Component Analysis (KPCA) method has large memories and high computational complexity in large datasets. A Greedy Kernel Principal Component Analysis (GKPCA) method is applied to reduce training data and deal with the nonlinear feature extraction problem for training data of large data in classification. First, a subset, which approximates to the original training data, is selected from the full training data using the greedy technique of the GKPCA method. Then, the feature extraction model is trained by the subset instead of the full training data. Finally, FCM algorithm classifies feature extraction data of the GKPCA, KPCA and PCA methods, respectively. The simulation results indicate that the feature extraction performance of both the GKPCA, and KPCA methods outperform the PCA method. In addition of retaining the performance of the KPCA method, the GKPCA method reduces computational complexity due to the reduced training set in classification.

Hot Rolling State Analysis Based on Kernel Principal Component Analysis and Information Entropy

2012 ◽  
Vol 572 ◽  
pp. 7-12
Author(s):  
Fei He ◽  
Quan Yang ◽  
Bao Jian Wang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Information Entropy ◽  
Hot Rolling ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Process Data ◽  
Feature Extraction Method ◽  
Information Metric

With more and more process data acquired from manufacturing process, extracting useful information to build empirical models of past successful operations is urgently required to get higher product quality. Clustering is the important data mining methods, where feature extraction is a significant factor to ensure the accurate rate of clustering and classification. As a common non-linear feature extraction method, kernel principal component analysis (KPCA) uses the variance as the information metric, but the variance is not always effective in some cases. Since information entropy is nonlinear and can effectively represent the dependencies of features, the Renyi entropy is used as the information metric to extract the feature in this paper. Simulation data, Tennessee Eastman and hot rolling process data are used for model validation. As a result the proposed method has better performance on feature extraction, compared with traditional KPCA.

scholarly journals Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak ◽  
Sridhar Krishnan ◽  
Pietro Lio
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Multiscale Methods ◽  
Kernel Principal Component Analysis ◽  
Simulation Data ◽  
Translation Invariant ◽  
Feature Extraction Method

We introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters required in the calculation of a kernel matrix. This new methodology incorporates multiscale methods into KPCA for transforming multiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in KPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered simulation data. We show that WKPCA is an effective feature extraction method for transforming a variety of multidimensional clustered data into data with a higher level of linearity among the data attributes. That brings an improvement in the accuracy of simple linear classifiers. Based on the analysis of the simulation data sets, we observe that multiscale translation invariant wavelet kernels for KPCA has an enhanced performance in feature extraction. The application of the proposed method to real data is also addressed.

A Bayesian Network Method for Automatic Classification of Eddy Current NDE Signals

2013 ◽  
Vol 291-294 ◽  
pp. 2775-2779
Author(s):  
Bo Ye ◽  
Fang Zeng ◽  
Ming Li
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Eddy Current ◽  
Structural Integrity ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Impedance Change ◽  
Noise Elimination ◽  
Better Than

Eddy current nondestructive evaluation (ECNDE) techniques are widely used in structural integrity and health monitoring. A novel algorithm was proposed for characterizing eddy current (EC) signals. In scanning inspection, the EC signals responding to impedance change were pre-processed for noise elimination and feature extraction. After feature extraction, Bayesian networks (BNs) were carried out to classify EC signals. It is shown by extensive experiments that kernel principal component analysis (KPCA) is better than principal component analysis (PCA) for feature extraction. The methods using BNs by KPCA feature extraction can perform better than the other classification methods.

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