Deep fisher discriminant analysis

Fisher Discriminant Analysis’ linear nature and the usual eigen-analysis approach to its solution have limited the application of its underlying elegant idea. In this work we will take advantage of some recent partially equivalent formulations based on standard least squares regression to develop a simple Deep Neural Network (DNN) extension of Fisher’s analysis that greatly improves on its ability to cluster sample projections around their class means while keeping these apart. This is shown by the much better accuracies and g scores of class mean classifiers when applied to the features provided by simple DNN architectures than what can be achieved using Fisher’s linear onesWith partial support from Spain's grants TIN2013-42351- P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAMCM. Work supported also by project FACIL{Ayudas Fundaci on BBVA a Equipos de Investigación Científica 2016, the UAM{ADIC Chair for Data Science and Machine Learning and Instituto de Ingeniería del Conocimiento. The third author is also supported by the FPU{MEC grant AP-2012-5163. We gratefully acknowledge the use of the facilities of Centro de Computacón Científi ca (CCC) at UA

A Novel Hybrid Dimensionality Reduction Method using Support Vector Machines and Independent Component Analysis

Due to the increasing demand for high dimensional data analysis from various applications such as electrocardiogram signal analysis and gene expression analysis for cancer detection, dimensionality reduction becomes a viable process to extracts essential information from data such that the high-dimensional data can be represented in a more condensed form with much lower dimensionality to both improve classification accuracy and reduce computational complexity. Conventional dimensionality reduction methods can be categorized into stand-alone and hybrid approaches. The stand-alone method utilizes a single criterion from either supervised or unsupervised perspective. On the other hand, the hybrid method integrates both criteria. Compared with a variety of stand-alone dimensionality reduction methods, the hybrid approach is promising as it takes advantage of both the supervised criterion for better classification accuracy and the unsupervised criterion for better data representation, simultaneously. However, several issues always exist that challenge the efficiency of the hybrid approach, including (1) the difficulty in finding a subspace that seamlessly integrates both criteria in a single hybrid framework, (2) the robustness of the performance regarding noisy data, and (3) nonlinear data representation capability. This dissertation presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) from Support Vector Machine (SVM) and data independence (unsupervised criterion) from Independent Component Analysis (ICA). The projection from SVM directly contributes to classification performance improvement in a supervised perspective whereas maximum independence among features by ICA construct projection indirectly achieving classification accuracy improvement due to better intrinsic data representation in an unsupervised perspective. For linear dimensionality reduction model, I introduce orthogonality to interrelate both projections from SVM and ICA while redundancy removal process eliminates a part of the projection vectors from SVM, leading to more effective dimensionality reduction. The orthogonality-based linear hybrid dimensionality reduction method is extended to uncorrelatedness-based algorithm with nonlinear data representation capability. In the proposed approach, SVM and ICA are integrated into a single framework by the uncorrelated subspace based on kernel implementation. Experimental results show that the proposed approaches give higher classification performance with better robustness in relatively lower dimensions than conventional methods for high-dimensional datasets

Multimodal Adversarial Learning

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models are susceptible to adversarial attacks. In the case of generative models, facial sketches drawn by artists mostly used by law enforcement agencies depend on the ability of the artist to clearly replicate all the key facial features that aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. However, the incorporation of hybrid discriminators, which perform attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network have shown to be powerful tools towards better multi modal learning techniques. In general, our overall approach was aimed at improving target detection systems and the visual appeal of synthesized images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesized image. We synthesized sketches using XDOG filter for the CelebA, Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results overall for different model applications are impressive compared to current state of the art

Hyperspectral Imaging for Landmine Detection

This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background

Multivariate Analysis in Management, Engineering and the Sciences

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field

Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique

Tesis por compendio[ES] El parto prematuro, definido como el nacimiento antes de las 37 semanas de gestación, es una importante preocupación mundial con implicaciones para la salud de los recién nacidos y los costes económicos. Afecta aproximadamente al 11% de todos los nacimientos, lo que supone más de 15 millones de individuos en todo el mundo. Los métodos actuales para predecir el parto prematuro carecen de precisión, lo que conduce a un sobrediagnóstico y a una viabilidad limitada en entornos clínicos. La electrohisterografía (EHG) ha surgido como una alternativa prometedora al proporcionar información relevante sobre la electrofisiología uterina. Sin embargo, los sistemas de predicción anteriores basados en EHG no se han trasladado de forma efectiva a la práctica clínica, debido principalmente a los sesgos en el manejo de datos desbalanceados y a la necesidad de modelos de predicción robustos y generalizables. Esta tesis doctoral pretende desarrollar un sistema de predicción del parto prematuro basado en inteligencia artificial utilizando EHG y datos obstétricos de mujeres sometidas a controles prenatales regulares. Este sistema implica la extracción de características relevantes, la optimización del subespacio de características y la evaluación de estrategias para abordar el reto de los datos desbalanceados para una predicción robusta. El estudio valida la eficacia de las características temporales, espectrales y no lineales para distinguir entre casos de parto prematuro y a término. Las nuevas medidas de entropía, en concreto la dispersión y la entropía de burbuja, superan a las métricas de entropía tradicionales en la identificación del parto prematuro. Además, el estudio trata de maximizar la información complementaria al tiempo que minimiza la redundancia y las características de ruido para optimizar el subespacio de características para una predicción precisa del parto prematuro mediante un algoritmo genético. Además, se ha confirmado la fuga de información entre el conjunto de datos de entrenamiento y el de prueba al generar muestras sintéticas antes de la partición de datos, lo que da lugar a una capacidad de generalización sobreestimada del sistema predictor. Estos resultados subrayan la importancia de particionar y después remuestrear para garantizar la independencia de los datos entre las muestras de entrenamiento y de prueba. Se propone combinar el algoritmo genético y el remuestreo en la misma iteración para hacer frente al desequilibrio en el aprendizaje de los datos mediante el enfoque de particio'n-remuestreo, logrando un área bajo la curva ROC del 94% y una precisión media del 84%. Además, el modelo demuestra un F1-score y una sensibilidad de aproximadamente el 80%, superando a los estudios existentes que consideran el enfoque de remuestreo después de particionar. Esto revela el potencial de un sistema de predicción de parto prematuro basado en EHG, permitiendo estrategias orientadas al paciente para mejorar la prevención del parto prematuro, el bienestar materno-fetal y la gestión óptima de los recursos hospitalarios. En general, esta tesis doctoral proporciona a los clínicos herramientas valiosas para la toma de decisiones en escenarios de riesgo materno-fetal de parto prematuro. Permite a los clínicos diseñar estrategias orientadas al paciente para mejorar la prevención y el manejo del parto prematuro. La metodología propuesta es prometedora para el desarrollo de un sistema integrado de predicción del parto prematuro que pueda mejorar la planificación del embarazo, optimizar la asignación de recursos y reducir el riesgo de parto prematuro.[CA] El part prematur, definit com el naixement abans de les 37 setmanes de gestacio', e's una important preocupacio' mundial amb implicacions per a la salut dels nounats i els costos econo¿mics. Afecta aproximadament a l'11% de tots els naixements, la qual cosa suposa me's de 15 milions d'individus a tot el mo'n. Els me¿todes actuals per a predir el part prematur manquen de precisio', la qual cosa condueix a un sobrediagno¿stic i a una viabilitat limitada en entorns cl¿'nics. La electrohisterografia (EHG) ha sorgit com una alternativa prometedora en proporcionar informacio' rellevant sobre l'electrofisiologia uterina. No obstant aixo¿, els sistemes de prediccio' anteriors basats en EHG no s'han traslladat de manera efectiva a la pra¿ctica cl¿'nica, degut principalment als biaixos en el maneig de dades desequilibrades i a la necessitat de models de prediccio' robustos i generalitzables. Aquesta tesi doctoral prete'n desenvolupar un sistema de prediccio' del part prematur basat en intel·lige¿ncia artificial utilitzant EHG i dades obste¿triques de dones sotmeses a controls prenatals regulars. Aquest sistema implica l'extraccio' de caracter¿'stiques rellevants, l'optimitzacio' del subespai de caracter¿'stiques i l'avaluacio' d'estrate¿gies per a abordar el repte de les dades desequilibrades per a una prediccio' robusta. L'estudi valguda l'efica¿cia de les caracter¿'stiques temporals, espectrals i no lineals per a distingir entre casos de part prematur i a terme. Les noves mesures d'entropia, en concret la dispersio' i l'entropia de bambolla, superen a les me¿triques d'entropia tradicionals en la identificacio' del part prematur. A me's, l'estudi tracta de maximitzar la informacio' complementa¿ria al mateix temps que minimitza la redunda¿ncia i les caracter¿'stiques de soroll per a optimitzar el subespai de caracter¿'stiques per a una prediccio' precisa del part prematur mitjan¿cant un algorisme gene¿tic. A me's, hem confirmat la fugida d'informacio' entre el conjunt de dades d'entrenament i el de prova en generar mostres sinte¿tiques abans de la particio' de dades, la qual cosa dona lloc a una capacitat de generalitzacio' sobreestimada del sistema predictor. Aquests resultats subratllen la importa¿ncia de particionar i despre's remostrejar per a garantir la independe¿ncia de les dades entre les mostres d'entrenament i de prova. Proposem combinar l'algorisme gene¿tic i el remostreig en la mateixa iteracio' per a fer front al desequilibri en l'aprenentatge de les dades mitjan¿cant l'enfocament de particio'-remostrege, aconseguint una a¿rea sota la corba ROC del 94% i una precisio' mitjana del 84%. A me's, el model demostra una puntuacio' F1 i una sensibilitat d'aproximadament el 80%, superant als estudis existents que consideren l'enfocament de remostreig despre's de particionar. Aixo¿ revela el potencial d'un sistema de prediccio' de part prematur basat en EHG, permetent estrate¿gies orientades al pacient per a millorar la prevencio' del part prematur, el benestar matern-fetal i la gestio' o¿ptima dels recursos hospitalaris. En general, aquesta tesi doctoral proporciona als cl¿'nics eines valuoses per a la presa de decisions en escenaris de risc matern-fetal de part prematur. Permet als cl¿'nics dissenyar estrate¿gies orientades al pacient per a millorar la prevencio' i el maneig del part prematur. La metodologia proposada e's prometedora per al desenvolupament d'un sistema integrat de prediccio' del part prematur que puga millorar la planificacio' de l'embara¿s, optimitzar l'assignacio' de recursos i millorar la qualitat de l'atencio'.[EN] Preterm delivery, defined as birth before 37 weeks of gestation, is a significant global concern with implications for the health of newborns and economic costs. It affects approximately 11% of all births, amounting to more than 15 million individuals worldwide. Current methods for predicting preterm labor lack precision, leading to overdiagnosis and limited practicality in clinical settings. Electrohysterography (EHG) has emerged as a promising alternative by providing relevant information about uterine electrophysiology. However, previous prediction systems based on EHG have not effectively translated into clinical practice, primarily due to biases in handling imbalanced data and the need for robust and generalizable prediction models. This doctoral thesis aims to develop an artificial intelligence based preterm labor prediction system using EHG and obstetric data from women undergoing regular prenatal check-ups. This system entails extracting relevant features, optimizing the feature subspace, and evaluating strategies to address the imbalanced data challenge for robust prediction. The study validates the effectiveness of temporal, spectral, and non-linear features in distinguishing between preterm and term labor cases. Novel entropy measures, namely dispersion and bubble entropy, outperform traditional entropy metrics in identifying preterm labor. Additionally, the study seeks to maximize complementary information while minimizing redundancy and noise features to optimize the feature subspace for accurate preterm delivery prediction by a genetic algorithm. Furthermore, we have confirmed leakage information between train and test data set when generating synthetic samples before data partitioning giving rise to an overestimated generalization capability of the predictor system. These results emphasize the importance of using partitioning-resampling techniques for ensuring data independence between train and test samples. We propose to combine genetic algorithm and resampling method at the same iteration to deal with imbalanced data learning using partition-resampling pipeline, achieving an Area Under the ROC Curve of 94% and Average Precision of 84%. Moreover, the model demonstrates an F1-score and recall of approximately 80%, outperforming existing studies on partition-resampling pipeline. This finding reveals the potential of an EHG-based preterm birth prediction system, enabling patient-oriented strategies for enhanced preterm labor prevention, maternal-fetal well-being, and optimal hospital resource management. Overall, this doctoral thesis provides clinicians with valuable tools for decision-making in preterm labor maternal-fetal risk scenarios. It enables clinicians to design a patient-oriented strategies for enhanced preterm birth prevention and management. The proposed methodology holds promise for the development of an integrated preterm birth prediction system that can enhance pregnancy planning, optimize resource allocation, and ultimately improve the outcomes for both mother and baby.Nieto Del Amor, F. (2023). Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/200900Compendi