Análisis multivariante: soluciones eficientes e interpretables

En la actualidad, existe una tendencia creciente de almacenar ingentes cantidades de datos con el fin de analizar y extraer algún tipo de información útil de ellos. Sin embargo, el tratamiento de los mismos no resulta trivial y la aplicación de métodos de análisis de datos puede sufrir multitud de problemas tales como sobreajuste o problemas de multicolinealidades causados por la existencia de variables altamente correladas. Por ello, una etapa previa de extracción de características que permita reducir la dimensionalidad de los datos y eliminar dichas multicolinealidades perjudiciales entre variables es crucial para poder aplicar de manera adecuada y eficiente dichas técnicas de análisis de datos. En particular, los métodos de análisis multivariante (MVA) –que permiten extraer un nuevo conjunto de características representativas del problema– gozan de amplia popularidad y han sido aplicados con éxito en una gran cantidad de aplicaciones del mundo real. No obstante, cuando el objetivo consiste en obtener conocimiento de los datos capturados, no solo se requieren buenas prestaciones del sistema diseñado, sino también la capacidad de producir soluciones interpretables que permitan una mejor comprensión del problema. Por lo tanto, resulta deseable modificar estos métodos MVA aportándoles una especialización de las necesidades del problema con el fin de obtener dicha interpretabilidad. En esta tesis doctoral, se estudian en detalle los métodos MVA y se presenta un marco general que engloba a dichos métodos MVA –en particular, a aquellos que obtienen características ortogonales entre sí–. Este estudio en profundidad permite una extensión de dicho marco general que facilita la inclusión de restricciones adicionales con el fin de proporcionarles habilidades adicionales, como, por ejemplo, la deseada capacidad de interpretabilidad. Para demostrar la versatilidad de este marco, se proponen soluciones MVA especializadas a cuatro casos particulares que requieren una interpretación completamente distinta del problema: soluciones MVA dispersas en las características extraídas; soluciones MVA dispersas en características extraídas a partir de relaciones no lineales entre variables; soluciones MVA que permiten la selección de las variables relevantes; y soluciones MVA no negativas para el diseño supervisado de bancos de filtros. Aunque en la literatura se pueden encontrar algunas soluciones especializadas, aquí se demuestra tanto teórica como experimentalmente que presentan graves problemas tanto de inicialización como de concepto en términos de poder ser considerados auténticos métodos MVA. La validez de las propuestas presentadas en esta tesis doctoral es certificada mediante una serie de experimentos que hacen uso de datos obtenidos del mundo real.Currently, there is a growing tendency to store large amounts of data to analyze and extract any useful information from them. However, treating them is not trivial and application of data analysis methods can suffer several problems such as overfitting or multicollinearity problems caused by the existence of highly correlated variables. Therefore, a preliminar feature extraction stage that reduces the dimensionality of the data and eliminates these harmful multicollinearities between variables is crucial to apply these techniques for data analysis in an appropriate and efficient way. In particular, multivariate analysis methods (MVA) –which allow to extract a new set of representative features of the problem– enjoy wide popularity and have been successfully applied in a large number of real-world applications. However, when the aim is to obtain knowledge of the captured data, and not just good performance of the designed system, the ability to produce interpretable solutions for a better understanding of the problem is required. Therefore, it is desirable to modify these MVA methods to provide them with specialization of problem needs to obtain such interpretability. In this thesis, we study in detail MVA methods and we present a general framework that encompasses them –in particular, those who obtain orthogonal features–. This in-depth study allows an extension of the general framework that facilitates the inclusion of additional constraints in order to provide additional properties, for example, the desired interpretability. To demonstrate the versatility of this framework, MVA specialized solutions to four particular cases that require completely different interpretation of the problem are proposed: sparse MVA solutions in the extracted features; sparse MVA solutions in extracted features from nonlinear relationships among variables; MVA solutions that allow the selection of the relevant variables; and non-negative MVA solutions for supervised design of filter banks. Although some specialized solutions can be found in the literature, here it is proven both theoretically and experimentally that they suffer serious problems of initialization and concept in terms of being considered authentic MVA methods. The legitimacy of the presented proposals in this thesis is certified through a series of experiments that use real-world data.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: José Luis Rojo Álvarez.- Secretario: José Miguel Leiva Murillo.- Vocal: Stevan Van Vaerenberg

A novel framework for parsimonious multivariate analysis

This paper proposes a framework in which a multivariate analysis method (MVA) guides a selection of input variables that leads to a sparse feature extraction. This framework, called parsimonious MVA, is specially suited for high dimensional data such as gene arrays, digital pictures, etc. The feature selection relies on the analysis of consistency in the behaviour of the input variables through the elements of an ensemble of MVA projection matrices. The ensemble is constructed following a bootstrap that builds on an efficient and generalized MVA formulation that covers PCA, CCA and OPLS. Moreover, it allows the estimation of the relative relevance of each selected input variable. Experimental results point out that the features extracted by the parsimonious MVA have excellent discrimination power, comparing favorably with state-of-the-art methods, and are potentially useful to build interpretable features. Besides, the parsimonious feature extractor is shown to be robust against to parameter selection, as we all computationally efficient.This work has been partly funded by the Spanish MINECO grant TEC2014-52289R and TEC2013-48439-C4-1-R. The authors want to thank the action editor and the reviewers for their valuable feedback

Regularized multivariate analysis framework for interpretable high-dimensional variable selection

Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data. One important property that is enjoyed by most such methods is uncorrelation among the extracted features. Recently, regularized versions of MVA methods have appeared in the literature, mainly with the goal to gain interpretability of the solution. In these cases, the solutions can no longer be obtained in a closed manner, and more complex optimization methods that rely on the iteration of two steps are frequently used. This paper recurs to an alternative approach to solve efficiently this iterative problem. The main novelty of this approach lies in preserving several properties of the original methods, most notably the uncorrelation of the extracted features. Under this framework, we propose a novel method that takes advantage of the,2,1 norm to perform variable selection during the feature extraction process. Experimental results over different problems corroborate the advantages of the proposed formulation in comparison to state of the art formulations.This work has been partly supported by MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R and TEC2016-75161-C2-2-R, and Comunidad de Madrid projects PRICAM P2013/ICE-2933 and S2013/ICE-2933

Nonnegative OPLS for supervised design of filter banks: application to image and audio feature extraction

Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals. However, most data analysis methods suffer from overfitting and numerical problems when data have more than a few dimensions needing a dimensionality reduction preprocessing. Moreover, interpretability about how and why filters work for audio or visual applications is a desired property, especially when energy or spectral signals are involved. In these cases, due to the nature of these signals, the nonnegativity of the filter weights is a desired property to better understand its working. Because of these two necessities, we propose different methods to reduce the dimensionality of data while the nonnegativity and interpretability of the solution are assured. In particular, we propose a generalized methodology to design filter banks in a supervised way for applications dealing with nonnegative data, and we explore different ways of solving the proposed objective function consisting of a nonnegative version of the orthonormalized partial least-squares method. We analyze the discriminative power of the features obtained with the proposed methods for two different and widely studied applications: texture and music genre classification. Furthermore, we compare the filter banks achieved by our methods with other state-of-the-art methods specifically designed for feature extraction.This work was supported in parts by the MINECO projects TEC2013-48439-C4-1-R, TEC2014-52289-R, TEC2016-75161-C2-1-R, TEC2016-75161-C2-2-R, TEC2016-81900-REDT/AEI, and PRICAM (S2013/ICE-2933)

Sparse and kernel OPLS feature extraction based on eigenvalue problem solving

Orthonormalized partial least squares (OPLS) is a popular multivariate analysis method to perform supervised feature extraction. Usually, in machine learning papers OPLS projections are obtained by solving a generalized eigenvalue problem. However, in statistical papers the method is typically formulated in terms of a reduced-rank regression problem, leading to a formulation based on a standard eigenvalue decomposition. A first contribution of this paper is to derive explicit expressions for matching the OPLS solutions derived under both approaches and discuss that the standard eigenvalue formulation is also normally more convenient for feature extraction in machine learning. More importantly, since optimization with respect to the projection vectors is carried out without constraints via a minimization problem, inclusion of penalty terms that favor sparsity is straightforward. In the paper, we exploit this fact to propose modified versions of OPLS. In particular, relying on the ℓ1 norm, we propose a sparse version of linear OPLS, as well as a non-linear kernel OPLS with pattern selection. We also incorporate a group-lasso penalty to derive an OPLS method with true feature selection. The discriminative power of the proposed methods is analyzed on a benchmark of classification problems. Furthermore, we compare the degree of sparsity achieved by our methods and compare them with other state-of-the-art methods for sparse feature extraction.This work was partly supported by MINECO projects TEC2011-22480 and PRIPIBIN-2011-1266.Publicad

Macroalocações de recursos em saúde por intermédio da atividade jurídico-processual: implicações bioéticas

A finalidade deste artigo é descrever a situação relativa à macroalocação de recursos de origem jurídico-processual gerada no Brasil pela nova conformação jurídico-política derivada da Constituição Federal de 1988 e analisar suas implicações na esfera da bioética. No que tange à bioética, a questão é analisada sob o ponto de vista dos princípios da beneficência, autonomia e justiça. Conclui-se que a situação descrita implica em violação do princípio da justiça pelo uso abusivo do princípio da beneficência. Conclui- se ainda que o abuso se concretiza pela ausência de participação da sociedade nas decisões judiciais, o que afasta o componente solidariedade. Conclui-se, paralelamente, que é necessário mudar a conformação jurídico- política com melhor definição dos poderes do juiz na esfera do Direito Sanitário e ainda que a situação reforça o modelo econômico que atrela os gastos em saúde aos interesses do mercado

Optimization of multi-classifiers for computational biology: application to gene finding and expression

Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods’ aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods’ aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further.Ministry of Science and Innovation, Spain (MICINN) Spanish Government TIN-2006-12879Junta de Andalucia TIC-02788Howard Hughes Medical InstituteEuropean Commission Junta de Andaluci

La motivación organizacional, herramienta de mejora continua. Un diagnóstico de la situación actúa del área de Producción de Crear Impresión Offset Digital SAS.

El principal objetivo de la investigación es resaltar la importancia de la motivación como base fundamental dentro de una organización para mejorar el desempeño de los colaboradores y así mismo lograr los objetivos organizacionales. El principal objetivo sustentar la falta de motivación en la planta de producción de la empresa Crear impresión Offset Digital S.A.S constituida legalmente desde el años 2006 registrada ante la Cámara de Comercio, compuesta por 20 empleados de los cuales 9 hacen parte del departamento de producción, que es nuestro enfoque de estudio, al presentar un nivel considerable de desmotivación. Esta organización cuenta con 12 años de experiencia en el mercado, trayectoria mediante la cual ha logrado posesionarse dentro del mismo con clientes muy representativos que le han permitido fortalecerse como proveedor, entre ellos (Claro-Telmex, Productos Roche, Banco Agrario y Banco BBVA, entre otros). Cuenta con personal de confianza que le permite tener estabilidad en su mano de obra, sin embargo al catalogarse como una pequeña empresa por su número de empleados, la alta gerencia deja de lado la importancia de motivarlos para mejorar su rendimiento. Al ser una empresa pequeña, no genera la necesidad en la alta gerencia de invertir en motivación al personal, donde se evidencie que la relación entre empleador y empleado no es únicamente salarial si no que además es importante su bienestar y desarrollo personal. La empresa tiene empleados muy antiguos, situación que permite tergiversar el verdadero valor de la motivación, porque se confunde el empoderamiento con el supuesto de bienestar, lo que hace que se convierta en el campo preciso a estudiar para brindar herramientas estructuradas y comparables que justifiquen la necesidad de implementar la motivación organizacional sin que se tenga en cuenta el número de empleados con el que cuente la empresa.The main objective of our research is to highlight the importance of motivation as a fundamental basis within an organization, to improve the performance of the collaborators and achieve the organizational objectives. We have as main objective to support the lack of motivation in the production departament of the company to Crear Impresión Offset Digital S.A. S, constituted legally since the years 2006 registered in the chamber of commerce, composed by 20 employees of which 10 make Part of the production department, which is our focus of study, by presenting a considerable level of demotivation. This organization has 12 years of experience in the market, trajectory that has allowed take within it, with very representative clients that have allowed it to strengthen as supplier, among them (Claro-Telmex, Products Roche, Agrario Bank and BBVA Bank, among others) has trusted staff that allows you to have stability in your workforce, however by cataloguing as a small company by its number of employees, the high management leaves out the importance of motivating them to improve their Performance. Being a company with "few" employees, does not generate need in management to invest in a project of motivation, which allows them to feel that the type of relationship that exists between them (employer-employee) is only salary or the need to generate an income, instead of generating a satisfaction by achieving the organizational goals in a mutual way. The company has very old employees, which allows to distort the true value of motivation, because it is confused with empowerment, which makes it become the precise field work to study to provide tools while structured and compared to justify the need to implement organizational motivation without taking into account the number of employees with whom the company counts. Currently there is a cooling of the working environment due to different factors among which it is highlighted; The economic crisis that crosses the country and the scourge of venezuelan migration to our country, specific reasons that make senior management decide to reduce staff and hire a cheaper workforce, generating staff demotivation and reduction in good performance of your work, generating repetitive and constant mistakes. We seek, to determine the level of the current intrinsic motivation of the production plant of this company, by means of a model of measurement previously designed by our team of work, with which it is intended to expose the importance of maintaining a personal motivated. The companies that motivate their employees are stable and recognized by their personnel, this is fundamental basis to achieve their own objectives and work in a didactic and friendly way, allowing to benefit all those who are part of that team. The organizational motivation is not an expense, it is a social responsibility, in the midst of a globalized world where only sees the human being as a tool of work and that day after day is replaced by the machines