Clustering of multiple instance data.

An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is referred to as a target concept (TC). Existing methods either only identify a single target concept, do not provide a mechanism for selecting the appropriate number of target concepts, or do not provide a flexible representation for target concept memberships. Thus, they are not suitable to handle data with large intra-class variation. In this dissertation we propose new algorithms that learn multiple target concepts simultaneously. The proposed algorithms combine concepts from data clustering and multiple instance learning. In particular, we propose crisp, fuzzy, and possibilistic variations of the Multi-target concept Diverse Density (MDD) metric, along with three algorithms to optimize them. Each algorithm relies on an alternating optimization strategy that iteratively refines concept assignments, locations, and scales until it converges to an optimal set of target concepts. We also demonstrate how the possibilistic MDD metric can be used to select the appropriate number of target concepts for a dataset. Lastly, we propose the construction of classifiers based on embedded feature space theory to use our target concepts to predict the label of prospective MIL data. The proposed algorithms are implemented, tested, and validated through the analysis of multiple synthetic and real-world data. We first demonstrate that our algorithms can detect multiple target concepts reliably, and are robust to many generative data parameters. We then demonstrate how our approach can be used in the application of Buried Explosive Object (BEO) detection to locate distinct target concepts corresponding to signatures of varying BEO types. We also demonstrate that our classifier strategies can perform competitively with other well-established embedded space approaches in classification of Benchmark MIL data

DEEP LEARNING METHODS FOR MULTIBAND EXPLOSIVE HAZARD DETECTION USING L-BAND AND X-BAND FORWARD-LOOKING GROUND-PENETRATING RADAR

Explosive hazards are one of the most deadly threats in modern conflicts. The U.S. Army is interested in a reliable way to detect these hazards at range. A promising way of accomplishing this task is using a forward-looking ground-penetrating radar (FLGPR) system. Recently, the Army has been testing a system that utilizes both L-band and X-band radar arrays on a vehicle mounted platform. Using data from this system, we sought to improve the performance of a constant false-alarm-rate (CFAR) prescreener through the use of three deep learning architechtures; deep belief networks (DBNs), stacked denoising autoencoders (SDAEs), and convolutional neural networks (CNNs). We also compare these deep learning classifiers with two more conventional shallow learning classifiers; single kernel support vector machines (SKSVMs) and multiple kernel learning group lasso (MKLGL). By training the deep learners on a combination of image features and comparing the test results to the conventional shallow learners, we were able to significantly increase the probability of detection over both the CFAR prescreener and the shallow learners while maintaining a nominal number of false alarms per square meter. Our research shows that deep learners are a good candidate for improving detection rates in FLGPR systems

Multiple instance fuzzy inference.

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Fuzzy Inference Systems (MI-FIS). Fuzzy inference is a powerful modeling framework that can handle computing with knowledge uncertainty and measurement imprecision effectively. Fuzzy Inference performs a non-linear mapping from an input space to an output space by deriving conclusions from a set of fuzzy if-then rules and known facts. Rules can be identified from expert knowledge, or learned from data. In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. In this dissertation, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, different multiple instance fuzzy inference styles are proposed. The Multiple Instance Mamdani style fuzzy inference (MI-Mamdani) extends the standard Mamdani style inference to compute with multiple instances. The Multiple Instance Sugeno style fuzzy inference (MI-Sugeno) is an extension of the standard Sugeno style inference to handle reasoning with multiple instances. In addition to the MI-FIS inference styles, one of the main contributions of this work is an adaptive neuro-fuzzy architecture designed to handle bags of instances as input and capable of learning from ambiguously labeled data. The proposed architecture, called Multiple Instance-ANFIS (MI-ANFIS), extends the standard Adaptive Neuro Fuzzy Inference System (ANFIS). We also propose different methods to identify and learn fuzzy if-then rules in the context of MIL. In particular, a novel learning algorithm for MI-ANFIS is derived. The learning is achieved by using the backpropagation algorithm to identify the premise parameters and consequent parameters of the network. The proposed framework is tested and validated using synthetic and benchmark datasets suitable for MIL problems. Additionally, we apply the proposed Multiple Instance Inference to the problem of region-based image categorization as well as to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar

Otimização multi-objetivo em aprendizado de máquina

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Regressão logística multinomial regularizada, classificação multi-rótulo e aprendizado multi-tarefa são exemplos de problemas de aprendizado de máquina em que objetivos conflitantes, como funções de perda e penalidades que promovem regularização, devem ser simultaneamente minimizadas. Portanto, a perspectiva simplista de procurar o modelo de aprendizado com o melhor desempenho deve ser substituída pela proposição e subsequente exploração de múltiplos modelos de aprendizado eficientes, cada um caracterizado por um compromisso (trade-off) distinto entre os objetivos conflitantes. Comitês de máquinas e preferências a posteriori do tomador de decisão podem ser implementadas visando explorar adequadamente este conjunto diverso de modelos de aprendizado eficientes, em busca de melhoria de desempenho. A estrutura conceitual multi-objetivo para aprendizado de máquina é suportada por três etapas: (1) Modelagem multi-objetivo de cada problema de aprendizado, destacando explicitamente os objetivos conflitantes envolvidos; (2) Dada a formulação multi-objetivo do problema de aprendizado, por exemplo, considerando funções de perda e termos de penalização como objetivos conflitantes, soluções eficientes e bem distribuídas ao longo da fronteira de Pareto são obtidas por um solver determinístico e exato denominado NISE (do inglês Non-Inferior Set Estimation); (3) Esses modelos de aprendizado eficientes são então submetidos a um processo de seleção de modelos que opera com preferências a posteriori, ou a filtragem e agregação para a síntese de ensembles. Como o NISE é restrito a problemas de dois objetivos, uma extensão do NISE capaz de lidar com mais de dois objetivos, denominada MONISE (do inglês Many-Objective NISE), também é proposta aqui, sendo uma contribuição adicional que expande a aplicabilidade da estrutura conceitual proposta. Para atestar adequadamente o mérito da nossa abordagem multi-objetivo, foram realizadas investigações mais específicas, restritas à aprendizagem de modelos lineares regularizados: (1) Qual é o mérito relativo da seleção a posteriori de um único modelo de aprendizado, entre os produzidos pela nossa proposta, quando comparado com outras abordagens de modelo único na literatura? (2) O nível de diversidade dos modelos de aprendizado produzidos pela nossa proposta é superior àquele alcançado por abordagens alternativas dedicadas à geração de múltiplos modelos de aprendizado? (3) E quanto à qualidade de predição da filtragem e agregação dos modelos de aprendizado produzidos pela nossa proposta quando aplicados a: (i) classificação multi-classe, (ii) classificação desbalanceada, (iii) classificação multi-rótulo, (iv) aprendizado multi-tarefa, (v) aprendizado com multiplos conjuntos de atributos? A natureza determinística de NISE e MONISE, sua capacidade de lidar adequadamente com a forma da fronteira de Pareto em cada problema de aprendizado, e a garantia de sempre obter modelos de aprendizado eficientes são aqui pleiteados como responsáveis pelos resultados promissores alcançados em todas essas três frentes de investigação específicasAbstract: Regularized multinomial logistic regression, multi-label classification, and multi-task learning are examples of machine learning problems in which conflicting objectives, such as losses and regularization penalties, should be simultaneously minimized. Therefore, the narrow perspective of looking for the learning model with the best performance should be replaced by the proposition and further exploration of multiple efficient learning models, each one characterized by a distinct trade-off among the conflicting objectives. Committee machines and a posteriori preferences of the decision-maker may be implemented to properly explore this diverse set of efficient learning models toward performance improvement. The whole multi-objective framework for machine learning is supported by three stages: (1) The multi-objective modelling of each learning problem, explicitly highlighting the conflicting objectives involved; (2) Given the multi-objective formulation of the learning problem, for instance, considering loss functions and penalty terms as conflicting objective functions, efficient solutions well-distributed along the Pareto front are obtained by a deterministic and exact solver named NISE (Non-Inferior Set Estimation); (3) Those efficient learning models are then subject to a posteriori model selection, or to ensemble filtering and aggregation. Given that NISE is restricted to two objective functions, an extension for many objectives, named MONISE (Many Objective NISE), is also proposed here, being an additional contribution and expanding the applicability of the proposed framework. To properly access the merit of our multi-objective approach, more specific investigations were conducted, restricted to regularized linear learning models: (1) What is the relative merit of the a posteriori selection of a single learning model, among the ones produced by our proposal, when compared with other single-model approaches in the literature? (2) Is the diversity level of the learning models produced by our proposal higher than the diversity level achieved by alternative approaches devoted to generating multiple learning models? (3) What about the prediction quality of ensemble filtering and aggregation of the learning models produced by our proposal on: (i) multi-class classification, (ii) unbalanced classification, (iii) multi-label classification, (iv) multi-task learning, (v) multi-view learning? The deterministic nature of NISE and MONISE, their ability to properly deal with the shape of the Pareto front in each learning problem, and the guarantee of always obtaining efficient learning models are advocated here as being responsible for the promising results achieved in all those three specific investigationsDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétrica2014/13533-0FAPES

CMUT based chemical sensor for classification and quantification with machine learning in a real-world application

In a quest for further enhancing human senses, chemical sensors are developed. Chemical sensors are proved to diagnose diseases, classify and quantify chemical warfare agents as well as measuring air pollution down to parts per billion [1-3]. Connecting multiple devices in large networks can help authorities and governments respond faster and make better decisions considering the release of emissions and/or dangerous gases. In order to create such networks, an inexpensive, robust and portable sensor must be developed. The chemical capacitive micromachined ultrasonic transducer (CMUT) might be such a sensor. This thesis demonstrates a proof of concept for a CMUT based chemical sensor as a gas detecting unit that can classify and quantify chemicals with machine learning in a real-world application. The CMUT is a sensor consisting of an array of polymer coated cells adsorbing different gases. Adsorption causes a frequency shift in the sensor output. This shift can be correlated to chemicals and their concentrations through machine learning. Reference data collected for the machine learning models was identified as a time-consuming process. An autosampler was devised, reducing time and cost related to the data collection. The CMUT sensor was tested in a greenhouse for 4 weeks to measure CO2 concentration in a plant bed under varying conditions. Testing the following statement: If the sensor can detect low concentrations of CO2 in ambient air it can also detect other compounds. The machine learning models were trained on the collected samples, and later compared to find the best model. The results showed that the CMUT sensor successfully measured CO2 down to 120 ppm in ambient air, the machine learning models could classify between high and low concentrations. For classification purposes the neural network with relu activation showed the best results, with a 15% error for both high and low concentrations. Quantification of the data had poor performance due to sensor drift. Large RMSE scores was found for all quantification models. The drift is most likely caused by the breakdown of the polymer, causing a frequency shift. The dataset was unbalanced and had a higher distribution on lower concentrations. Which to some extent undermine the results from the machine learning, although giving an indication of sensor performance. Further research is recommended to assess the polymer coating on the CMUT as well as removing drift. Reducing the size of the sensor and equipment, as well as connecting the sensor to a cloud database, is recommended and identified as important steps for creating a sensor network.I søken etter å forbedre menneskets sanser ønsker man å utvikle kjemiske sensorer. Kjemiske sensorer har blitt brukt til å diagnostisere sykdommer, klassifisere og kvantifisere nervegass i tillegg til å måle luftforurensing som har svært lav oppløsning. Ved å sette sammen flere elektroniske neser i større nettverk vil det bidra med økt informasjon om utslipp i byer. Dette vil hjelpe myndigheter med å ta bedre og raskere beslutninger for å unngå spredning av farlige kjemikalier og/eller forurensning. For å lage slike nettverk må sensorene som benyttes være pålitelige, kostnadseffektive og robuste. En sensor som oppfyller disse kravene er den kjemiske kapasitive mikromaskinerte ultralyd transduceren (CMUT).M-MP

Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals

The work collected in this dissertation addresses the problem of data fusion. In other words, this is the problem of making decisions (also known as the problem of classification in the machine learning and statistics communities) when data from multiple sources are available, or when decisions/confidence levels from a panel of decision-makers are accessible. This problem has become increasingly important in recent years, especially with the ever-increasing popularity of autonomous systems outfitted with suites of sensors and the dawn of the ``age of big data.\u27\u27 While data fusion is a very broad topic, the work in this dissertation considers two very specific techniques: feature-level fusion and decision-level fusion. In general, the fusion methods proposed throughout this dissertation rely on kernel methods and fuzzy integrals. Both are very powerful tools, however, they also come with challenges, some of which are summarized below. I address these challenges in this dissertation. Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher-dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used to learn the aggregation of a set of valid kernels into a single (ideally) superior kernel. The aggregation can be done using weighted sums of the pre-computed kernels, but determining the summation weights is not a trivial task. Furthermore, MKL does not work well with large datasets because of limited storage space and prediction speed. These challenges are tackled by the introduction of many new algorithms in the following chapters. I also address MKL\u27s storage and speed drawbacks, allowing MKL-based techniques to be applied to big data efficiently. Some algorithms in this work are based on the Choquet fuzzy integral, a powerful nonlinear aggregation operator parameterized by the fuzzy measure (FM). These decision-level fusion algorithms learn a fuzzy measure by minimizing a sum of squared error (SSE) criterion based on a set of training data. The flexibility of the Choquet integral comes with a cost, however---given a set of N decision makers, the size of the FM the algorithm must learn is 2N. This means that the training data must be diverse enough to include 2N independent observations, though this is rarely encountered in practice. I address this in the following chapters via many different regularization functions, a popular technique in machine learning and statistics used to prevent overfitting and increase model generalization. Finally, it is worth noting that the aggregation behavior of the Choquet integral is not intuitive. I tackle this by proposing a quantitative visualization strategy allowing the FM and Choquet integral behavior to be shown simultaneously

IST Austria Thesis

Traditionally machine learning has been focusing on the problem of solving a single task in isolation. While being quite well understood, this approach disregards an important aspect of human learning: when facing a new problem, humans are able to exploit knowledge acquired from previously learned tasks. Intuitively, access to several problems simultaneously or sequentially could also be advantageous for a machine learning system, especially if these tasks are closely related. Indeed, results of many empirical studies have provided justification for this intuition. However, theoretical justifications of this idea are rather limited. The focus of this thesis is to expand the understanding of potential benefits of information transfer between several related learning problems. We provide theoretical analysis for three scenarios of multi-task learning - multiple kernel learning, sequential learning and active task selection. We also provide a PAC-Bayesian perspective on lifelong learning and investigate how the task generation process influences the generalization guarantees in this scenario. In addition, we show how some of the obtained theoretical results can be used to derive principled multi-task and lifelong learning algorithms and illustrate their performance on various synthetic and real-world datasets

Modelling, Simulation and Data Analysis in Acoustical Problems

Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years

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