Myoelectric signal classification based on S transform and two-directional two-dimensional principal component analysis

2017 ◽  
Vol 40 (7) ◽  
pp. 2387-2395 ◽  
Author(s):  
Yi Ji ◽  
Hong-Bo Xie
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
S Transform ◽  
Efficiency And Effectiveness

Time-frequency representiation has been intensively employed for the analysis of biomedical signals. In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis (PCA). This study contributes a two-directional two-dimensional principal component analysis (2D2PCA)-based technique for time-frequency feature extraction. The S transform, integrating the strengths of short time Fourier transform and wavelet transform, is applied to perform the time-frequency decomposition. Then, 2D2PCA is directly conducted on the time-frequency matrix rather than 1D vectors for feature extraction. The proposed method can significantly reduce the computational cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using 4-channel myoelectric signals recorded in health subjects and amputees.

Matrix Based Feature Measurement and Extraction for Face Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 643-647
Author(s):  
Qi Zhu ◽  
Jin Rong Cui ◽  
Zi Zhu Fan
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Face Recognition ◽  
Measurement Method ◽  
Principal Component ◽  
Component Analysis ◽  
High Accuracy ◽  
Face Image ◽  
Two Dimensional ◽  
The Matrix

In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.

scholarly journals An Incremental Two-Dimensional Principal Component Analysis for Object Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Weimin Ge ◽  
Mingyue Sun ◽  
Xiaofeng Wang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Object Recognition ◽  
Convergence Rates ◽  
Principal Component ◽  
Component Analysis ◽  
Video Stream ◽  
Two Dimensional ◽  
Batch Mode ◽  
Time Requirements

Two-dimensional principal component analysis algorithm (2DPCA) can be performed in the batch mode and can not meet the real-time requirements of the video stream. To overcome these limitations, the incremental learning of the candid covariance-free incremental PCA (CCIPCA) is innovated to the existing 2DPCA, and the called incremental 2DPCA (I2DPCA) is firstly presented to incrementally compute the principal components of a sequence of samples directly on the 2D image matrices without estimating the covariance matrices. Therefore, the I2DPCA can improve the feature extraction speed and reduce the required memory. However, the variations between the column direction, generally neglected, are also useful for the high-accuracy object recognition. Thus, another incremental sequential row-column 2DPCA algorithm (IRC2DPCA), based on the proposed I2DPCA algorithm, is also proposed. The IRC2DPCA can compress the image matrices in the row and column direction, and the feature matrices extracted by the IRC2DPCA are with less dimensions than the I2DPCA. The substantial experimental results show that the IRC2DPCA, compared with the other three algorithms, can improve the convergence rates and the recognition rates, compress the dimensions of the feature matrices, and reduce the feature extraction time and the classification time.

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.

Two-Directional Two-Dimensional Principal Component Analysis Based on Wavelet Decomposition for High-Dimensional Biomedical Signals Classification

2017 ◽  
pp. 100-122
Author(s):  
Hong-Bo Xie ◽  
Tianruo Guo
Keyword(s):  
Principal Component Analysis ◽  
Multiple Scales ◽  
Essential Feature ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Two Dimensional ◽  
Multi Scale ◽  
Efficiency And Effectiveness ◽  
The Time Domain

Here, we present a multi-scale two-directional two-dimensional principal component analysis (MS2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify twenty hand motions using 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis. With this multi-scale two-directional two-dimensional principal component analysis for high-dimensional signal classification, spatial-time-frequency discriminant information from high-dimensional EMG electrode array can be effectively extracted and reduced using the proposed method. Compared with the time domain feature extraction in conjunction with PCA, MS2D2PCA performed better with higher classification accuracy and less PCs in EMG classification. The efficiency and effectiveness of the method can be further validated by using high-dimensional EEG, MEG, fMRI signals. Although the present study focuses on high-dimensional signal pattern classification, based on the PCs obtained at multiple scales, it is relatively straightforward to expand MS2D2PCA for high-dimensional signal compression, denoising, component extraction, and other related tasks.

Two-Directional Two-Dimensional Principal Component Analysis Based on Wavelet Decomposition for High-Dimensional Biomedical Signals Classification

2018 ◽  
pp. 232-254
Author(s):  
Hong-Bo Xie ◽  
Tianruo Guo
Keyword(s):  
Principal Component Analysis ◽  
Multiple Scales ◽  
Essential Feature ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Two Dimensional ◽  
Multi Scale ◽  
Efficiency And Effectiveness ◽  
The Time Domain

Here, we present a multi-scale two-directional two-dimensional principal component analysis (MS2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify twenty hand motions using 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis. With this multi-scale two-directional two-dimensional principal component analysis for high-dimensional signal classification, spatial-time-frequency discriminant information from high-dimensional EMG electrode array can be effectively extracted and reduced using the proposed method. Compared with the time domain feature extraction in conjunction with PCA, MS2D2PCA performed better with higher classification accuracy and less PCs in EMG classification. The efficiency and effectiveness of the method can be further validated by using high-dimensional EEG, MEG, fMRI signals. Although the present study focuses on high-dimensional signal pattern classification, based on the PCs obtained at multiple scales, it is relatively straightforward to expand MS2D2PCA for high-dimensional signal compression, denoising, component extraction, and other related tasks.

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