ICA mixture model based unsupervised classification of hyperspectral imagery

Independent Component Analysis (ICA) is a blind source separation method that has proven popular in many fields of application. ICA can be improved incorporating temporal dependencies creating dynamic ICA methods and defining subspaces with multiple ICAs. Such a dynamic ICA method is called Sequential Independent Component Analysis Mixture Model (SICAMM). This method is proposed for two new EEG signal processing applications: detection of arousals in apnea patients and brain hemisphere activity classification during a memory task. SICAMM is compared with a nondynamic ICAMM model and a Dynamic Bayesian Network (DBN). Results show that SICAMM obtains a better performance than the DBN and both dynamic methods achieve a higher classification rate than the stationary ICAMM model. Furthermore, the structure of the SICAMM parameters suggests it for extraction of significant clinical information.

This thesis is aimed at finding solutions and statistical modeling techniques to analyze the video content in a way such that intelligent and efficient interaction with video is possible. In our work, we investigate several fundamental tasks for content analysis of video. Specifically, we propose an outline video parsing algorithm using basic statistical measures and an off-line solution using Independent Component Analysis (ICA). A spatiotemporal video similarity model based on dynamic programming is developed. For video object segmentation and tracking, we develop a new method based on probabilistic fuzzy c-means and Gibbs random fields. Theoretically, we develop a generic framework for sequential data analysis. The new framework integrates both Hidden Markov Model and ICA mixture model. The re-estimation formulas for model parameter learning are also derived. As a case study, the new model is applied to golf video for semantic event detection and recognition.

Support Vector Machines (SVM) and Independent Component Analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.KeywordsSupport Vector MachineFace RecognitionFace ImageIndependent Component AnalysisFace DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The traditional procedure for increasing the signal-tonoise ratio of NMR echo trains is based on stacking and then averaging the data acquired at different depths up to a certain depth interval. Although this works efficiently in increasing the signal-to-noise ratio, it also reduces the vertical resolution of the acquired signal. Therefore a trade-off has to be made between cancelling the noise level without deteriorating the targeted signal but yet still achieving acceptable vertical and spectral resolutions. In NMR logging technology, vertical resolution, usually assessed by NMR-total porosity compared to neutrondensity porosity, is important. However, noise can also affect the spectral distribution of the inverted signal that generates the T1 or T 2 distribution and its components like: clay-bound water, irreducible water, and free fluid. In the area of machine learning applied to denoising, relatively new algorithm developments are using approaches like ICA (Independent Component Analysis), which are based on exploring the statistical independency of the signal components. ICA in fact, puts a stronger mathematical condition on the data-independency—rather than perpendicularity (which traditional Principal Component Analysis (PCA) is based on). However, since ICA is a kind of blind source separation algorithm, the subspace of the ICA solution is usually determined by the relevant numbers of principal components obtained after running PCA. There are several ICA approaches proposed in the literature. One of the most popular is the ‘FastIca’ algorithm. We choose the FastIca algorithm for our test due to its ease of implementation in the MATLAB computational environment. The results from simulated data show that ICA-denoising can be more effective than stacking, if the numbers of the blindly defined ICA components are properly selected. The tradeoff between preserving a significant amount of signal and filtering the right amount of noise is a sensitive aspect in implementing ICA for denoising when rock-fluid characteristics are being explored. Results from real data show that ICA can be readily adapted for post-processing in log analysis, but the selection of ICA-subspace poses a major challenge.

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. 

Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many other applications such as channel equalization, speech processing. Recently, it has been shown that the ICA can be also used for hyperspectral data compression. This paper investigates these two transforms in hyperspectral data compression and further evaluates their strengths and weaknesses in applications of target detection, mixed pixel classification and abundance quantification. In order to take advantage of the strengths of both transform, a new transform, called mixed PCA/ICA transform is developed in this paper. The idea of the proposed mixed PCA/ICA transform is derived from the fact that it can integrate different levels of information captured by the PCA and ICA. In doing so, it combines m principal components (PCs) resulting from the PCA and n independent components (ICs) generated by the ICA to form a new set of (m+n) mixed components used for hyperspectral data compression. The resulting transform is referred to as mixed (m,n)-PCA/ICA transform. In order to determine the total number of components, p needed to be generated for the mixed (m,n)-PCA/ICA transform, a recently developed virtual dimensionality (VD) is introduced to estimate the p where p = m + n. If m = p and n = 0, then mixed (m,n)-PCA/ICA transform is reduced to PCA transform. On the other hand, if m = 0 and n = p, then mixed (m,n)-PCA/ICA transform is reduced to ICA. Since various combinations of m and n have different impacts on the performance of the mixed PCA/ICA spectral/spatial compression in applications, experiments based on subpixel detection and mixed pixel quantification are conducted for performance evaluation.

Kernel-based approaches have recently drawn considerable interests in hyperspectral image analysis due to its ability in expanding features to a higher dimensional space via a nonlinear mapping function. Many well-known detection and classification techniques such as Orthogonal Subspace Projection (OSP), RX algorithm, linear discriminant analysis, Principal Components Analysis (PCA), Independent Component Analysis (ICA), have been extended to the corresponding kernel versions. Interestingly, a target detection method, called Constrained Energy Minimization (CEM) which has been also widely used in hyperspectral target detection has not been extended to its kernel version. This paper investigates a kernel-based CEM, called Kernel CEM (K-CEM) which employs various kernels to expand the original data space to a higher dimensional feature space that CEM can be operated on. Experiments are conducted to perform a comparative analysis and study between CEM and K-CEM. The results do not show K-CEM provided significant improvement over CEM in detecting hyperspectral targets but does show significant improvement in detecting targets in multispectral imagery which provides limited spectral information for the CEM to work well.

In this manuscript, we propose a sparse self-representation (SSR) method to select a band subset in hyperspectral imagery (HSI) classification. The SSR method improves from multiple measurement vectors (MMV) with the measurement matrix equals to the observation matrix. The SSR regards that each band could be represented as a linear combination of the representatives of all bands and accordingly all band vectors are reconstructed by themselves accompanied with a sparse coefficient matrix. The SSR finds the indices of nonzero rows of the coefficient matrix by convex optimization to help to estimate the band subset that are constituted with representatives. Preliminary classification results on two open HSI datasets show that the SSR offers comparable results to the linear constrained minimum variance-based band correlation constraint (LCMV-BCC) method, and achieves better results than the maximum-variance principal component analysis (MVPCA) and sparse based band selection (SPaBS) methods.

Face recognition finds extensive applications in various places. To build a fully functional and reliable system for recognizing faces, a robust and efficient face detection algorithm is required. Existing systems incorporate geometrical and template based approaches like distance between different points on the face, shape of the face, matching with existing templates and similar methods. These methods work successfully with specific images and have a tendency to fail, when an unusual image is tested. To overcome this, we propose statistical feature based approach in this study for face recognition. For this purpose, we make a detailed study of various feature extraction techniques involving principal and independent component analysis. Four different classifiers have been for the extracted features. Accuracy measurements and execution time have been recorded for the analysis. It is found that factor analysis method outperforms principal component analysis and independent component analysis with improved accuracy.

This paper describes the application of principal component analysis (PCA) and independent component analysis (ICA) to identify the reference spectra of a pharmaceutical tablet's constituent compounds from Raman spectroscopic data. The analysis shows, first with a simulated data set and then with data collected from a pharmaceutical tablet, that both PCA and ICA are able to identify most of the features present in the reference spectra of the constituent compounds. However, the results suggest that the ICA method may be more appropriate when attempting to identify unknown reference spectra from a sample. The resulting PCA and ICA models are subsequently used to estimate the relative concentrations of the constituent compounds and to produce spatial distribution images of the analyzed tablet. These images provide a visual representation of the spatial distribution of the constituent compounds throughout the tablet. Images associated with the ICA scores are found to be more informative and not as affected by measurement noise as the PCA based score images. The paper concludes with a discussion of the future work that needs to be undertaken for ICA to gain wider acceptance in the applied spectroscopy community.

Data compression on the illumination adjustable images by PCA and ICA

Different classification techniques are being designed and under usage for classification of hyper spectral images. The usage of these classifiers differ for different type of hyper spectral data and application domain. The performance of these classifiers are influenced by feature preprocessing stage. In this research work we have investigated the impact of feature preprocessing using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on classification stage. The experiment is carried out using three sets of hyperspectral data. Classifications were carried out using three different classification techniques namely Maximum Likelihood Estimation (MLE), Constraint Energy Minimization (CEM) and Spectral Angle Mapper (SAM) on the preprocessed hyperspectral data. It is observed that the impact of PCA and LDA on the classification techniques are in two aspects (a) The preprocessing techniques facilitates to achieve high classification accuracy even with less number of training features and (b) Preprocessing expedites the classification process for large data sets. Also it can be concluded that PCA outperforms LDA in case of noisy data.

With the development of nondestructive detection, the emerging testing techniques provide new challenges to signal analysis and interpretation approach applied to the inspection evaluation. Some researchers have developed the methods that focus on feature analysis of detected signals. This article presents a new feature analysis by the Independent Component Analysis (ICA) approach to evaluate the defects tested by the pulsed eddy current (PEC) technique. ICA is a high-order statistics technique used to separate multi-unknown sources, which has been successfully applied to facial image identification and separation of the components of 1D signal. In this article, the ICA approach is utilized to project the response signals of various defects into the independent components (ICs) feature subspace by signal representation model. Dependent on the selected ICs, each defect is represented by different projected coefficients, which are proposed to discriminate and classify the defects that belong to three categories. The improved ICA model is proposed to improve the classification of two similar categories of single defects: metal loss and subsurface defects. The evaluation using the series of experimental data has validated the classification of single defects and the defects with lift-off effect by our ICA approach. The comparison with Principal component analysis (PCA)–based approach further verified the better performance of the ICA-based model.

Unlike Vehicle-mounted ground penetrating radar (GPR), the hand-held GPR data is highly variable. In this paper we propose an independent component analysis (ICA) based approach for processing hand held stepped frequency GPR data for mine detection. ICA is a linear transformation, which seeks prominent features in high-dimensional data. Compared to principal component analysis (PCA), which searches for basis vectors in the direction of maximum variance, ICA finds more interesting features in the direction of maximum non-gaussianity. In our current implementation, ICA is used to find a set of basis vectors corresponding to the background clutter. Residual error for this GPR with respect to ICA clutter basis shows the presence or absence of landmine. The performance of the ICA based detection is compared with the correlation detector for GPR only data for hand held mine detection. Comparative receiver operating characteristics (ROC) curves representing probability of detection verses false alarm rate are shown for both scan and investigative mode for ICA based detection and correlation detection.