scholarly journals Spectral Feature Extraction Based on the DCPCA Method

2013 ◽  
Vol 30 ◽  
Author(s):  
BU YUDE ◽  
PAN JINGCHANG ◽  
JIANG BIN ◽  
CHEN FUQIANG ◽  
WEI PENG
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Spectral Feature ◽  
Cataclysmic Variables ◽  
Principal Component ◽  
Support Vector ◽  
Sparse Principal Component Analysis ◽  
Astronomical Data ◽  
Feature Lines ◽  
Feature Extraction Technique

AbstractIn this paper, a new sparse principal component analysis (SPCA) method, called DCPCA (sparse PCA using a difference convex program), is introduced as a spectral feature extraction technique in astronomical data processing. Using this method, we successfully derive the feature lines from the spectra of cataclysmic variables. We then apply this algorithm to get the first 11 sparse principal components and use the support vector machine (SVM) to classify. The results show that the proposed method is comparable with traditional methods such as PCA+SVM.

scholarly journals Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tsun-Kuo Lin
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
High Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Image Features ◽  
Image Feature ◽  
Support Vector ◽  
Feature Identification ◽  
Adaptive Pca

This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

scholarly journals An Efficient Forgery Image Detection Method using Hybrid Feature Extraction and Multiclass SVM

2019 ◽  
Vol 8 (2S2) ◽  
pp. 278-284
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Principal Component ◽  
Extraction Technique ◽  
Gray Level ◽  
Image Forgery ◽  
Hybrid Feature Extraction ◽  
Occurrence Matrix ◽  
Feature Extraction Technique

The advancement of image editing software tools in the image processing field has led to an exponential increase in the manipulation of the images. Subjective differentiation of original and manipulated images has become almost impossible. This has kindled the interest among researchers to develop algorithms for detecting the forgery in the image. ImageSplicing, Copy-Move and Image Retouching are the most common image forgery techniques. The existing methods to detect image forgery has drawbacks like false detection, high execution time and low accuracy rate. Considering these issues, this work proposes an efficient method for detection of image forgery. Initially, bilateral filter is used to remove the noise in pre-processing, Chan-Vese Segmentation algorithm is used to detect the clumps from the filtered image utilizing both intensity and edgeinformation, followed by hybrid feature extraction technique. Hybrid feature extraction technique comprises of Dual Tree Complex-Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Gray-Level-Co-Occurrence Matrix (GLCM). The DWT has dual-tree complex wavelet transform with important properties, it is nearly shift invariant and directionally selective in two and higher dimensions. Principal Component Analysis (PCA) finds the eigenvectors of a covariance matrix with the highest eigenvalues and uses these values to project the data into a new subspace of equal or less dimensions. Gray-Level-Co-Occurrence Matrix (GLCM) extracts the Feature values such as energy, entropy, homogeneity, standard deviation, variance, contrast, correlation and mean. Classification is done based on the texture values of training dataset and testing dataset using Multi Class-Support Vector Machine (SVM). The performance analysis is done based on the True positive, False positive and True negative values. The experimental results obtained using the proposed technique shows a better performance compared to the existing KNN classifier model.

Gas pipeline small leakage feature extraction based on LMD envelope spectrum entropy and PCA–RWSVM

2016 ◽  
Vol 38 (12) ◽  
pp. 1460-1470 ◽  
Author(s):  
Lina Wang ◽  
Xianwen Gao ◽  
Tan Liu
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Support Vector ◽  
Local Mean Decomposition ◽  
Natural Gas Pipelines ◽  
Product Function ◽  
Vector Machines ◽  
Local Mean ◽  
Envelope Spectrum

This paper presents a novel intelligent method based on local mean decomposition and multi-class reproducing wavelet support vector machines (RWSVMs), which are applied to detect leakage in natural gas pipelines. First, local mean decomposition is used to construct product function components to decompose the leakage signals. Then, we select the leakage signals which contain the most leakage information, according to the kurtosis features of these signals, through principal component analysis. Next, we reconstruct the principal product function components in order to acquire the envelope spectrum. Finally, we confirm the leak aperture by inputting envelope spectrum entropy features, as feature vectors, into the RWSVMs. Through analysing the pipeline leakage signals, the experiments show that this method can effectively identify different leak categories.

Nuclear norm based two-dimensional sparse principal component analysis

2018 ◽  
Vol 16 (02) ◽  
pp. 1840002 ◽  
Author(s):  
Yudong Chen ◽  
Zhihui Lai ◽  
Jiajun Wen ◽  
Can Gao
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction Methods ◽  
Reconstruction Error ◽  
Nuclear Norm ◽  
Two Dimensional ◽  
Sparse Principal Component Analysis ◽  
New Feature

Two-Dimensional Principal Component Analysis (2D-PCA) is one of the most simple and effective feature extraction methods in the field of pattern recognition. However, the traditional 2D-PCA lacks robustness and the function of sparse feature extraction. In this paper, we propose a new feature extraction approach based on the traditional 2D-PCA, which is called Nuclear Norm Based Two-Dimensional Sparse Principal Component Analysis (N-2D-SPCA). To improve the robustness of 2D-PCA, we utilize nuclear norm to measure the reconstruction error of loss function. At the same time, we obtain sparse feature extraction by adding [Formula: see text]-norm and [Formula: see text]-norm regularization terms to the model. By designing an alternatively iterative algorithm, we can solve the optimization problem and learn a projection matrix for use with feature extraction. Besides, we present a bilateral projections model (BN-2D-SPCA) to further compress the dimensions of the feature matrix. We verify the effectiveness of our method on four benchmark face databases including AR, ORL, FERET and Yale databases. Experimental results show that the proposed method is more robust than some state-of-the-art methods and the traditional 2D-PCA.

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