Support vector machines with constraints for sparsity in the primal parameters

This paper introduces 1 a new support vector machine (SVM) formulation to obtain sparse solutions in the primal SVM parameters, providing a new method for feature selection based on SVMs. This new approach includes additional constraints to the classical ones that drop the weights associated to those features that are likely to be irrelevant. A !-SVM formulation has been used, where ! indicates the fraction of features to be considered. This paper presents two versions of the proposed sparse classifier, a 2-norm SVM and a 1-norm SVM, the latter having a reduced computational burden with respect to the first one. Additionally, an explanation is provided about how the presented approach can be readily extended to multiclass classification or to problems where groups of features, rather than isolated features, need to be selected. The algorithms have been tested in a variety of synthetic and real data sets and they have been compared against other state of the art SVM-based linear feature selection methods, such as 1-norm SVMand doubly regularized SVM. The results show the good feature selection ability of the approaches.This work was supported in part by the Ministry of Science and Innovation (Spanish Goverment), under Grant TEC2008-02473Publicad

Detection and prediction problems with applications in personalized health care

The United States health-care system is considered to be unsustainable due to its unbearably high cost. Many of the resources are spent on acute conditions rather than aiming at preventing them. Preventive medicine methods, therefore, are viewed as a potential remedy since they can help reduce the occurrence of acute health episodes. The work in this dissertation tackles two distinct problems related to the prevention of acute disease. Specifically, we consider: (1) early detection of incorrect or abnormal postures of the human body and (2) the prediction of hospitalization due to heart related diseases. The solution to the former problem could be used to prevent people from unexpected injuries or alert caregivers in the event of a fall. The latter study could possibly help improve health outcomes and save considerable costs due to preventable hospitalizations. For body posture detection, we place wireless sensor nodes on different parts of the human body and use the pairwise measurements of signal strength corresponding to all sensor transmitter/receiver pairs to estimate body posture. We develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test and Multiple Support Vector Machines. The measurements from the wireless sensor nodes are highly variable and these methods have different degrees of adaptability to this variability. Besides, these methods also handle multiple observations differently. Our analysis and experimental results suggest that GLT is more accurate and suitable for the problem. For hospitalization prediction, our objective is to explore the possibility of effectively predicting heart-related hospitalizations based on the available medical history of the patients. We extensively explored the ways of extracting information from patients' Electronic Health Records (EHRs) and organizing the information in a uniform way across all patients. We applied various machine learning algorithms including Support Vector Machines, AdaBoost with Trees, and Logistic Regression adapted to the problem at hand. We also developed a new classifier based on a variant of the likelihood ratio test. The new classifier has a classification performance competitive with those more complex alternatives, but has the additional advantage of producing results that are more interpretable. Following this direction of increasing interpretability, which is important in the medical setting, we designed a new method that discovers hidden clusters and, at the same time, makes decisions. This new method introduces an alternating clustering and classification approach with guaranteed convergence and explicit performance bounds. Experimental results with actual EHRs from the Boston Medical Center demonstrate prediction rate of 82% under 30% false alarm rate, which could lead to considerable savings when used in practice

Aplicación de técnicas de aprendizaje máquina para la caracterización y clasificación de pacientes con trastorno obsesivo compulsivo

El siguiente Trabajo Fin de Grado se basa en el cada vez más habitual empleo de métodos de aprendizaje máquina con el fin de clasificar y caracterizar trastornos psiquiátricos. Concretamente, el sistema diseñado pretende acercarse al diagnóstico de TOC (‘Trastorno Obsesivo Compulsivo’) a través del análisis de imágenes de resonancia magnética (MRI). El sistema diseñado tiene como objetivo plantear un algoritmo capaz de diagnosticar pacientes con TOC y, principalmente, capaz de caracterizar la enfermedad, detectando de manera automática las regiones neuroanatómicas relacionadas con el trastorno. Para ello, se empleará una arquitectura modular creada a partir de dos premisas fundamentales. 1. Análisis por áreas funcionales y/o neuroanatómicas. Cada imagen de resonancia magnética se divide en, aproximadamente, una centena de subconjuntos compuestos por vóxeles asociados a un área funcional o región neuroanatómica del cerebro. Así pues, el objetivo es aplicar un clasificador que facilite la selección de los conjuntos de vóxeles relevantes para la detección de la enfermedad. 2. Caracterización y fusión de áreas funcionales. El sistema utilizará métodos de selección de características sobre las salidas de los clasificadores el objetivo de obtener una selección automática de las áreas relevantes para el diagnóstico de la patología que estamos tratando. Asimismo, el último paso será el estudio de la relación que tienen las áreas entre sí mediante el uso de clasificadores, tanto lineales como no lineales. Una vez desarrollado y aplicado el algoritmo, se aprovecharán los resultados tanto para comparar la clasificación de pacientes con los resultados previos obtenidos mediante métodos tradicionales [1], [2], como para analizar el patrón de áreas neuroanatómicas responsables del trastorno. -------------------------------------------------------This work is based on increasingly common use of machine learning methods in order to classify and characterize psychiatric disorders. Specifically, the designed system tries to be able to diagnose OCD (Obsessive-Compulsive Disorder) though the MRI (Magnetic Resonance Imaging) analysis. The main system's goal is to construct an algorithm able to detect OCD patients and characterize the disease, detecting automatically neuroanatomical regions related to the disorder, supported on a modular arquitecture process with two fundamental principles. 1. Analysis of functional and/or neuroanatomical areas. Each MRI is divided into one hundred subsets composed of voxels associated to a functional area. Thus, the goal is to apply a classifier which facilitates the selection of the relevant voxels sets for the diagnosis of the disease. 2. Characterization and combination of functional areas. The system will use feature selection methods with the outputs of the first classifiers in order to get an automatic selection of the relevant areas for diagnosis of the pathology. The last step will use linear and no liner classifiers to analyze whether the different areas are interrelated. Having the algorithm developed, we will use the results to compare the classifications of patients with previous results got by traditional methods [1], [2], and to analyze the pattern of neuroanatomical areas responsible for the disorder.Ingeniería de Sistemas Audiovisuale

Ultrasonography for the prediction of musculoskeletal function

Ultrasound (US) imaging is a well-recognised technique for studying in vivo characteristics of a range of biological tissues due to its portability, low cost and ease of use; with recent technological advances that increased the range of choices regarding acquisition and analysis of ultrasound data available for studying dynamic behaviour of different tissues. This thesis focuses on the development and validation of methods to exploit the capabilities of ultrasound technology to investigate dynamic properties of skeletal muscles in vivo exclusively using ultrasound data. The overarching aim was to evaluate the influence of US data properties and the potential of inference algorithms for prediction of net ankle joint torques. A fully synchronised experimental setup was designed and implemented enabling collection of US, Electromyography (EMG) and dynamometer data from the Gastrocnemius medialis muscle and ankle joint of healthy adult volunteers. Participants performed three increasing complexity muscle movement tasks: passive joint rotations, isometric and isotonic contractions. Two frame rates (32 and 1000 fps) and two data precisions (8 and 16-bits) were obtained enabling analysis of the impact of US data temporal resolution and precision on joint torque predictions. Predictions of net joint torque were calculated using five data inference algorithms ranging from simple regression through to Artificial Neural Networks. Results indicated that accurate predictions of net joint torque can be obtained from the analysis of ultrasound data of one muscle. Significantly improved predictions were observed using the faster frame rate during active tasks, with 16-bit data precision and ANN further improving results in isotonic movements. The speed of muscle activation and complexity of fluctuations of the resulting net joint torques were key factors underpinning the prediction errors recorded. The properties of collected US data in combination with the movement tasks to be assessed should therefore be a key consideration in the development of experimental protocols for in vivo assessment of skeletal muscles