Face Recognition Using Independent Component Analysis and Support Vector Machines ⋆

Blind Signal Separation II. Linear, Convolutive Combinations: II. Linear, Convolutive Combinations

Not alike to principal component analysis (PCA) based monitoring statistics (T2 and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I2 , Ie 2 and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian distribution. Lee et al. (2004) proposed to use kernel density estimation (KDE) to obtain the control limits. However, the KDE method is very sensitive to the choice of smoothing parameter. Therefore, this study utilizes the support vector machine (SVM) for process fault detection by taking information of ICA extracted statistics as inputs of SVM. The proposed method (named, ICA-SVM) will be implemented in the Tennessee Eastman Process. In which, several multivariate monitoring schemes such as PCA, ICA, modified ICA and ICA- PCA will be also compared to demonstrate the efficiency of proposed method.

Epilepsy is a brain disease that causes people to have seizures over and over again. It is very hard to diagnose and treat. Finding epileptic seizures early is very important for getting help right away and improving the patient's result. This paper discussed about a way to find epileptic seizures using a mix of signal processing methods as feature extraction techniques, such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), along with different machine learning algorithms. The suggested framework is made up of several steps. First, noise and other errors are taken out of the raw EEG data. Next, PCA and ICA are used to pull out important features from the signals that have already been processed. These features are then fed into a number of machine learning methods to help them classify the data. The proposed framework uses Support Vector Machine (SVM), Random Forest, Convolutional Neural Networks (CNN), Long short-term memory (LSTM), CNN with BiLSTM, and Gradient Boosting, which are all machine learning methods used for correctness of proposed model. These methods are used to divide the EEG data into groups of seizures and non-seizures. A sample with EEG readings from people with epilepsy is used to test how well the system works. This dataset is used to train and test the framework. Performance measures like sensitivity, specificity, and accuracy are used to judge how well it works. According to the results, the suggested framework is very good at finding epileptic seizures. It combines PCA for feature extraction with a number of machine learning methods. The framework's flexible design makes it easy to add new features and methods and change how it works. 

Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field.

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.

Image recognition is a feature extraction and pattern matching technique to classify various objectives in computer science. In theory, it mainly relies on the images with distinguishable features as a good starting point. Each image has its own unique characteristics, and image features can be categorized into color features, texture features, and shape features etc. Therefore, using modern recognition techniques, we extract the image features through various algorithms to search for images with high similarities. There are many image recognition algorithms, and some of them are with high recognition rate while others are robust. But not all algorithms can be unconditionally implemented without adjusting to the real situation. In this paper, we introduce the classification algorithm based on Support Vector Machine (SVM) and the feature extraction method based on Principal Components Analysis (PCA). We employ the feature extraction algorithm to characterize facial features and recognize faces by comparing them to those stored in the training data set. Finally, we show the applications of feature extraction and classification algorithms in character recognition. The real characters printed on a cord are preprocessed by PCA, and then classified and identified by SVM with a good recognition rate if properly processed.

In this paper, we address the problem of blind speech signal separation. A new perceptual convolutive blind source separation algorithm is proposed based on filtered-E LMS algorithm and the masking properties of human auditory system. This algorithm emphasizes frequencies to which the human ear is sensitive and de-emphasizes frequencies that are inaudible to human ear by incorporating the properties of human auditory system making it suitable for speech separation. Simulation results on blind speech signal separation show that the proposed algorithm can improve the separated speech quality and has better convergence performance.

To solve the problem of single channel blind signal separation with unknown components number, a method based on Bayesian-MCMC is presented. First, single channel mixed signal, whose components have same general parameters, is modeled to change the problem into joint estimation of signal parameters and components number. Then, Bayesian theorem is applied in the joint estimation. Lastly, Bayesian computation of the estimation is accomplished with reversible jump MCMC. Simulation results indicate that the algorithm is practical and effective.

Independent component analysis (ICA) is a feature extraction technique for blind source separation. Enhanced independent component analysis (EICA), which has enhanced generalization performance, operates in a reduced principal component analysis (PCA) space. SVM is a powerful supervised learning algorithm, which is rooted in statistical learning theory. SVM has demonstrated high generalization capabilities in many pattern recognition problems. In this paper, we integrate EICA with SVM and apply this new method to the leak detection problem in oil pipelines. In features extraction, EICA produces EICA features of the original pressure images. In classification, SVM classified the EICA features as leak or non-leak. The test results based on real data indicate that the method can detect many leak faults from a pressure curve, and reduce the ratio of false and missing alarm than conventional methods.

Conventional remote sensing classification techniques that model the data in each class with a multivariate Gaussian distribution are inefficient, as this assumption is generally not valid in practice. We present a novel, independent component analysis (ICA) based approach for unsupervised classification of hyperspectral imagery. ICA, employed for a mixture model, estimates the data density in each class and models class distributions with nonGaussian structure, formulating the ICA mixture model (ICAMM). We apply the ICAMM for unsupervised classification of a test image from the AVIRIS sensor. Four feature extraction techniques namely principal component analysis, segmented principal component analysis, orthogonal subspace projection and projection pursuit have been considered as preprocessing steps for reducing the data dimensionality. The results demonstrate that the ICAMM significantly outperforms the K-means algorithm for land cover classification of hyperspectral imagery implemented on reduced data sets. Moreover, datasets extracted using segmented principal component analysis produce the highest classification accuracy.

Since the volume of the medical data is increasing due to the presence of vast amount of features, the conventional rule mining technique is not competent to handle the data and to perform precise diagnosis. For instance, this paper intends to implement the improved rule mining technique to overcome the above mentioned limitations. The model comes out with two main contribution stages (i) Using Map Reducing Framework (ii) Classification. Initially, the input medical data is given to map reduce framework. Here, Multi-linear Principal Component Analysis (MPCA) is used for reducing the given bulk data. Then, the reduced data is given to the classification process, where it classifies the disease with high accuracy. For this, this paper uses Support Vector Machine (SVM) classifier. After the completion of implementation, the proposed model compares its performance over other conventional methods like Principal Component Analysis- NN (PCA-NN), Independent Component Analysis- NN (ICA-NN), MPCA-NN, PCA-SVM and ICA-SVM, respectively in terms of performance measures like accuracy, specificity, sensitivity, precision, False Positive Rate (FPR), False Negative Rate (FNR), Net Present Value (NPV), False Discovery Rate (FDR), F1Score, and Mathews Correlation Coefficient (MCC), and the superiority of proposed model is proven over other methods.

Petroleum and its products are complex mixture, and how to precise analysis its components is an important part of the oil industry. In this paper, we proposed a components forecasting methods for gasoline octane value prediction based on independent component analysis (ICA) and support vector machine (SVM). By evaluating the accuracy of the models with two feature optimization methods(principal component analysis, PCA and successive projections algorithm, SPA) and two prediction models (neural network, ANN and minimum mean square squares, PLS). The results show that: the prediction relative error of the ICA-SVM model is only 0.34% and the squared correlation coefficient(R2) reached 0.99583, 0.97891, and the mean squared error(MSE) was 0.0010276, 0.013122 on the training set and test set respectively which are better than other compound models. This method in this paper has positive significance for the oil component analysis.

Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors

For sets of measurements does not follow a Gaussian distribution, the conventional principal component analysis (PCA) method has the disadvantage of low diagnostic yield. An integrated fault diagnosing method based on the independent component analysis (ICA) and support vector machine (SVM) was proposed. The observed data is preprocessed and feature extracted by ICA and a monitoring model was developed. When the fault is detected, SVM is adopted to classifying and diagnosing the type of faults. It is applied for fault diagnosing in the Three-Tank water level control system. The simulation results show that the fault diagnosis rates of this method is 99.8%, which can effectively detect and diagnose the fault.

Self-organizing feature maps are topologically ordered. One develops realistic cortical structures when given approximations of the visual environment as input, and are an effective way to model the development of face recognition abilities. Support vector machines (SVMs) are classifiers, which have demonstrated high generalization capabilities. In this paper, we combine these two techniques for face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates with relative low classification cost. As the results using the combination SOM/SVM were not very far from only with SVM, but the classifier cost of SOM/SVM is one-tenth of with SVM.