Kernel Fisher Discriminant Analysis Based on a Regularized Method for Multiclassification and Application in Lithological Identification

This study aimed to construct a kernel Fisher discriminant analysis (KFDA) method from well logs for lithology identification purposes. KFDA, via the use of a kernel trick, greatly improves the multiclassification accuracy compared with Fisher discriminant analysis (FDA). The optimal kernel Fisher projection of KFDA can be expressed as a generalized characteristic equation. However, it is difficult to solve the characteristic equation; therefore, a regularized method is used for it. In the absence of a method to determine the value of the regularized parameter, it is often determined based on expert human experience or is specified by tests. In this paper, it is proposed to use an improved KFDA (IKFDA) to obtain the optimal regularized parameter by means of a numerical method. The approach exploits the optimal regularized parameter selection ability of KFDA to obtain improved classification results. The method is simple and not computationally complex. The IKFDA was applied to the Iris data sets for training and testing purposes and subsequently to lithology data sets. The experimental results illustrated that it is possible to successfully separate data that is nonlinearly separable, thereby confirming that the method is effective

Assessment of a multi-measure functional connectivity approach

Efforts to find differences in brain activity patterns of subjects with neurological and psychiatric disorders that could help in their diagnosis and prognosis have been increasing in recent years and promise to revolutionise clinical practice and our understanding of such illnesses in the future. Resting-state functional magnetic resonance imaging (rsfMRI) data has been increasingly used to evaluate said activity and to characterize the connectivity between distinct brain regions, commonly organized in functional connectivity (FC) matrices. Here, machine learning methods were used to assess the extent to which multiple FC matrices, each determined with a different statistical method, could change classification performance relative to when only one matrix is used, as is common practice. Used statistical methods include correlation, coherence, mutual information, transfer entropy and non-linear correlation, as implemented in the MULAN toolbox. Classification was made using random forests and support vector machine (SVM) classifiers. Besides the previously mentioned objective, this study had three other goals: to individually investigate which of these statistical methods yielded better classification performances, to confirm the importance of the blood-oxygen-level-dependent (BOLD) signal in the frequency range 0.009-0.08 Hz for FC based classifications as well as to assess the impact of feature selection in SVM classifiers. Publicly available rs-fMRI data from the Addiction Connectome Preprocessed Initiative (ACPI) and the ADHD-200 databases was used to perform classification of controls vs subjects with Attention-Deficit/Hyperactivity Disorder (ADHD). Maximum accuracy and macro-averaged f-measure values of 0.744 and 0.677 were respectively achieved in the ACPI dataset and of 0.678 and 0.648 in the ADHD-200 dataset. Results show that combining matrices could significantly improve classification accuracy and macro-averaged f-measure if feature selection is made. Also, the results of this study suggest that mutual information methods might play an important role in FC based classifications, at least when classifying subjects with ADHD

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Algorithms for Neural Prosthetic Applications

abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization

As a complicated ubiquitous phenomenon encountered in ultrasound imaging, speckle can be treated as either annoying noise that needs to be reduced or the source from which diagnostic information can be extracted to reveal the underlying properties of tissue. In this study, the application of Independent Component Analysis (ICA), a relatively new statistical signal processing tool appeared in recent years, to both the speckle texture analysis and despeckling problems of B-mode ultrasound images was investigated. It is believed that higher order statistics may provide extra information about the speckle texture beyond the information provided by first and second order statistics only. However, the higher order statistics of speckle texture is still not clearly understood and very difficult to model analytically. Any direct dealing with high order statistics is computationally forbidding. On the one hand, many conventional ultrasound speckle texture analysis algorithms use only first or second order statistics. On the other hand, many multichannel filtering approaches use pre-defined analytical filters which are not adaptive to the data. In this study, an ICA-based multichannel filtering texture analysis algorithm, which considers both higher order statistics and data adaptation, was proposed and tested on the numerically simulated homogeneous speckle textures. The ICA filters were learned directly from the training images. Histogram regularization was conducted to make the speckle images quasi-stationary in the wide sense so as to be adaptive to an ICA algorithm. Both Principal Component Analysis (PCA) and a greedy algorithm were used to reduce the dimension of feature space. Finally, Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel were chosen as the classifier for achieving best classification accuracy. Several representative conventional methods, including both low and high order statistics based methods, and both filtering and non-filtering methods, have been chosen for comparison study. The numerical experiments have shown that the proposed ICA-based algorithm in many cases outperforms other algorithms for comparison. Two-component texture segmentation experiments were conducted and the proposed algorithm showed strong capability of segmenting two visually very similar yet different texture regions with rather fuzzy boundaries and almost the same mean and variance. Through simulating speckle with first order statistics approaching gradually to the Rayleigh model from different non-Rayleigh models, the experiments to some extent reveal how the behavior of higher order statistics changes with the underlying property of tissues. It has been demonstrated that when the speckle approaches the Rayleigh model, both the second and higher order statistics lose the texture differentiation capability. However, when the speckles tend to some non-Rayleigh models, methods based on higher order statistics show strong advantage over those solely based on first or second order statistics. The proposed algorithm may potentially find clinical application in the early detection of soft tissue disease, and also be helpful for better understanding ultrasound speckle phenomenon in the perspective of higher order statistics. For the despeckling problem, an algorithm was proposed which adapted the ICA Sparse Code Shrinkage (ICA-SCS) method for the ultrasound B-mode image despeckling problem by applying an appropriate preprocessing step proposed by other researchers. The preprocessing step makes the speckle noise much closer to the real white Gaussian noise (WGN) hence more amenable to a denoising algorithm such as ICS-SCS that has been strictly designed for additive WGN. A discussion is given on how to obtain the noise-free training image samples in various ways. The experimental results have shown that the proposed method outperforms several classical methods chosen for comparison, including first or second order statistics based methods (such as Wiener filter) and multichannel filtering methods (such as wavelet shrinkage), in the capability of both speckle reduction and edge preservation

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

Vision-based hand shape identification for sign language recognition

This thesis introduces an approach to obtain image-based hand features to accurately describe hand shapes commonly found in the American Sign Language. A hand recognition system capable of identifying 31 hand shapes from the American Sign Language was developed to identify hand shapes in a given input image or video sequence. An appearance-based approach with a single camera is used to recognize the hand shape. A region-based shape descriptor, the generic Fourier descriptor, invariant of translation, scale, and orientation, has been implemented to describe the shape of the hand. A wrist detection algorithm has been developed to remove the forearm from the hand region before the features are extracted. The recognition of the hand shapes is performed with a multi-class Support Vector Machine. Testing provided a recognition rate of approximately 84% based on widely varying testing set of approximately 1,500 images and training set of about 2,400 images. With a larger training set of approximately 2,700 images and a testing set of approximately 1,200 images, a recognition rate increased to about 88%

Efficient Learning Machines

Computer scienc

Discharge Moisture Prediction of the Corn Gluten Feed Drying Process Using Machine Learning Algorithms

Modern manufacturing processes have multiple sensors (or instruments) installed that provide constant data stream outputs; however, there are some critical performance and quality variables where installing physical sensors is either impractical, expensive, not hardy enough for hostile environments or the sensor technology is not sufficiently advanced. An example of such a problem is measure moisture of solid products in real-time. If this scenario happens, Machine Learning (ML) approaches are a suitable solution as are capable of learning and representing complex relationships. ML algorithms establish a mathematical relationship between the quantity of interest and other measurable quantities such as readings from already available sensors (e.g., SCADA, historian softwares, SQL Databases, etc.). This study details how ML algorithms (Such as Multiple Linear Regression, Support Vector Machine Regression and Regression Trees) are used to predict critical variable moisture in gluten feed (a by-product of the wet-milling of maize grain for starch or ethanol production) as a simple, robust and fast solution for the lack of this variable real-time information for a corn products manufacturer. The resulting model performance demonstrates the feasibility of the ML algorithms approach to predict moisture behaviour