Advances in Nonnegative Matrix Decomposition with Application to Cluster Analysis

Nonnegative Matrix Factorization (NMF) has found a wide variety of applications in machine learning and data mining. NMF seeks to approximate a nonnegative data matrix by a product of several low-rank factorizing matrices, some of which are constrained to be nonnegative. Such additive nature often results in parts-based representation of the data, which is a desired property especially for cluster analysis.  This thesis presents advances in NMF with application in cluster analysis. It reviews a class of higher-order NMF methods called Quadratic Nonnegative Matrix Factorization (QNMF). QNMF differs from most existing NMF methods in that some of its factorizing matrices occur twice in the approximation. The thesis also reviews a structural matrix decomposition method based on Data-Cluster-Data (DCD) random walk. DCD goes beyond matrix factorization and has a solid probabilistic interpretation by forming the approximation with cluster assigning probabilities only. Besides, the Kullback-Leibler divergence adopted by DCD is advantageous in handling sparse similarities for cluster analysis.  Multiplicative update algorithms have been commonly used for optimizing NMF objectives, since they naturally maintain the nonnegativity constraint of the factorizing matrix and require no user-specified parameters. In this work, an adaptive multiplicative update algorithm is proposed to increase the convergence speed of QNMF objectives.  Initialization conditions play a key role in cluster analysis. In this thesis, a comprehensive initialization strategy is proposed to improve the clustering performance by combining a set of base clustering methods. The proposed method can better accommodate clustering methods that need a careful initialization such as the DCD.  The proposed methods have been tested on various real-world datasets, such as text documents, face images, protein, etc. In particular, the proposed approach has been applied to the cluster analysis of emotional data

Deep Clustering and Deep Network Compression

The use of deep learning has grown increasingly in recent years, thereby becoming a much-discussed topic across a diverse range of fields, especially in computer vision, text mining, and speech recognition. Deep learning methods have proven to be robust in representation learning and attained extraordinary achievement. Their success is primarily due to the ability of deep learning to discover and automatically learn feature representations by mapping input data into abstract and composite representations in a latent space. Deep learning’s ability to deal with high-level representations from data has inspired us to make use of learned representations, aiming to enhance unsupervised clustering and evaluate the characteristic strength of internal representations to compress and accelerate deep neural networks.Traditional clustering algorithms attain a limited performance as the dimensionality in-creases. Therefore, the ability to extract high-level representations provides beneficial components that can support such clustering algorithms. In this work, we first present DeepCluster, a clustering approach embedded in a deep convolutional auto-encoder. We introduce two clustering methods, namely DCAE-Kmeans and DCAE-GMM. The DeepCluster allows for data points to be grouped into their identical cluster, in the latent space, in a joint-cost function by simultaneously optimizing the clustering objective and the DCAE objective, producing stable representations, which is appropriate for the clustering process. Both qualitative and quantitative evaluations of proposed methods are reported, showing the efficiency of deep clustering on several public datasets in comparison to the previous state-of-the-art methods.Following this, we propose a new version of the DeepCluster model to include varying degrees of discriminative power. This introduces a mechanism which enables the imposition of regularization techniques and the involvement of a supervision component. The key idea of our approach is to distinguish the discriminatory power of numerous structures when searching for a compact structure to form robust clusters. The effectiveness of injecting various levels of discriminatory powers into the learning process is investigated alongside the exploration and analytical study of the discriminatory power obtained through the use of two discriminative attributes: data-driven discriminative attributes with the support of regularization techniques, and supervision discriminative attributes with the support of the supervision component. An evaluation is provided on four different datasets.The use of neural networks in various applications is accompanied by a dramatic increase in computational costs and memory requirements. Making use of the characteristic strength of learned representations, we propose an iterative pruning method that simultaneously identifies the critical neurons and prunes the model during training without involving any pre-training or fine-tuning procedures. We introduce a majority voting technique to compare the activation values among neurons and assign a voting score to evaluate their importance quantitatively. This mechanism effectively reduces model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Empirically, we demonstrate that our pruning method is robust across various scenarios, including fully-connected networks (FCNs), sparsely-connected networks (SCNs), and Convolutional neural networks (CNNs), using two public datasets.Moreover, we also propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts, with the aim of evaluating the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a se-mantic concept and individual hidden unit representations is utilized to evaluate feature maps’ importance quantitatively. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines.To conclude, we present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a founding contribution to the area of applying deep clustering to time-series data by presenting the first case study in the context of movement behavior clustering utilizing the DeepCluster method. The results are promising, showing that the latent space encodes sufficient patterns to facilitate accurate clustering of movement behaviors. Finally, we identify state-of-the-art and present an outlook on this important field of DTSC from five important perspectives

Describing Images by Semantic Modeling using Attributes and Tags

This dissertation addresses the problem of describing images using visual attributes and textual tags, a fundamental task that narrows down the semantic gap between the visual reasoning of humans and machines. Automatic image annotation assigns relevant textual tags to the images. In this dissertation, we propose a query-specific formulation based on Weighted Multi-view Non-negative Matrix Factorization to perform automatic image annotation. Our proposed technique seamlessly adapt to the changes in training data, naturally solves the problem of feature fusion and handles the challenge of the rare tags. Unlike tags, attributes are category-agnostic, hence their combination models an exponential number of semantic labels. Motivated by the fact that most attributes describe local properties, we propose exploiting localization cues, through semantic parsing of human face and body to improve person-related attribute prediction. We also demonstrate that image-level attribute labels can be effectively used as weak supervision for the task of semantic segmentation. Next, we analyze the Selfie images by utilizing tags and attributes. We collect the first large-scale Selfie dataset and annotate it with different attributes covering characteristics such as gender, age, race, facial gestures, and hairstyle. We then study the popularity and sentiments of the selfies given an estimated appearance of various semantic concepts. In brief, we automatically infer what makes a good selfie. Despite its extensive usage, the deep learning literature falls short in understanding the characteristics and behavior of the Batch Normalization. We conclude this dissertation by providing a fresh view, in light of information geometry and Fisher kernels to why the batch normalization works. We propose Mixture Normalization that disentangles modes of variation in the underlying distribution of the layer outputs and confirm that it effectively accelerates training of different batch-normalized architectures including Inception-V3, Densely Connected Networks, and Deep Convolutional Generative Adversarial Networks while achieving better generalization error

Applications of Machine Learning to Optimizing Polyolefin Manufacturing

This chapter is a preprint from our book by , focusing on leveraging machine learning (ML) in chemical and polyolefin manufacturing optimization. It's crafted for both novices and seasoned professionals keen on the latest ML applications in chemical processes. We trace the evolution of AI and ML in chemical industries, delineate core ML components, and provide resources for ML beginners. A detailed discussion on various ML methods is presented, covering regression, classification, and unsupervised learning techniques, with performance metrics and examples. Ensemble methods, deep learning networks, including MLP, DNNs, RNNs, CNNs, and transformers, are explored for their growing role in chemical applications. Practical workshops guide readers through predictive modeling using advanced ML algorithms. The chapter culminates with insights into science-guided ML, advocating for a hybrid approach that enhances model accuracy. The extensive bibliography offers resources for further research and practical implementation. This chapter aims to be a thorough primer on ML's practical application in chemical engineering, particularly for polyolefin production, and sets the stage for continued learning in subsequent chapters. Please cite the original work [169,170] when referencing

Estruturas hierárquicas orientadas por dados em aprendizado multi-tarefa

Orientador: Fernando José Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Em aprendizado multi-tarefa, um conjunto de tarefas é simultaneamente considerado durante o processo de aprendizado de modo a promover ganho de desempenho através da exploração de similaridades entre tarefas. Em um número significativo de abordagens, tais similaridades são codificadas como informação adicional na etapa de regularização. Embora algumas estruturas sejam levadas em consideração em muitas propostas, como a existência de grupos de tarefas ou um relacionamento baseado em grafo, outras propostas mostraram que usar uma estrutura hierárquica corretamente definida poderá guiar a resultados competitivos. Focando em um relacionamento hierárquico, a extensão buscada nesta pesquisa é baseada na ideia de aprender a estrutura diretamente dos dados, possibilitando que a metodologia multi-tarefa possa ser estendida a uma gama mais vasta de aplicações. Assim, a hipótese levantada é que obter um relacionamento representativo dos dados baseado em hierarquia entre tarefas e usar esta informação adicional como um termo de penalização dentro do formalismo de aprendizado regularizado seria benéfico, relaxando a necessidade de um especialista específico de domínio e melhorando o desempenho de predição. Portanto, a novidade em abordagens hierárquicas orientadas por dados propostas nesta dissertação para aprendizado multi-tarefa é que a troca de informação entre tarefas reais associadas é promovida por tarefas hipotéticas auxiliares presentes nos nós mais altos, dado que as tarefas reais não são diretamente conectadas na hierarquia. Uma vez que a ideia principal envolve obter uma estrutura hierárquica, estudos foram feitos com foco em combinar ambas as áreas de clusterização hierárquica e aprendizado multi-tarefa. Três estratégias promissoras para a obtenção automática de estruturas hierárquicas foram adaptadas ao contexto de aprendizado multi-tarefa. Duas delas são abordagens Bayesianas, sendo uma caracterizada por ramificações não binárias. A possibilidade de corte na estrutura também é investigada, sendo uma poderosa ferramenta para detecção de tarefas outliers. Além disso, um conceito geral chamado Hierarchical Multi-Task Learning Framework é proposto, agrupando módulos individualmente, os quais podem ser facilmente estendidos em pesquisas futuras. Experimentos extensivos são apresentados e discutidos, mostrando o potencial da utilização de estruturas hierárquicas obtidas diretamente dos dados para guiar a etapa de regularização. Foram adotados nos experimentos tanto conjuntos de dados sintéticos com relacionamento entre tarefas conhecido como conjuntos de dados reais utilizados na literatura, nos quais foi possível observar que o framework proposto consistentemente supera estratégias bem estabelecidas de aprendizado multi-tarefaAbstract: In multi-task learning, a set of learning tasks is simultaneously considered during the learning process so that it can leverage performance by exploring similarities among the tasks. In a significant number of approaches, such similarities are encoded as additional information within the regularization framework. Although some sort of structure is taken into account by several proposals, such as the existence of task clusters or a graph-based relationship, others have shown that using a properly defined hierarchical structure may lead to competitive results. Focusing on a hierarchical relationship, the extension pursued in this research is based on the idea of learning it directly from data, enabling a methodology like this to be extended to a wider range of applications. Thus, the hypothesis raised is that obtaining a representative hierarchy-based task relationship from data and using this additional information as a penalty term in the regularization framework would be beneficial, relaxing the necessity of a domain-specific specialist and improving overall generalization predictive performance. Therefore, the novelty of the data-driven hierarchical approaches proposed in this dissertation for multi-task learning is that information exchange among associated real tasks is promoted by auxiliary hypothetical tasks at the upper nodes, given that the real tasks are not directly connected in the hierarchy. Once the main idea involves obtaining a hierarchical structure, several studies were performed focusing on combining both hierarchical clustering and multi-task learning areas. Three promising strategies for automatically obtaining hierarchical structures were adapted to the context of multi-task learning. Two of them are Bayesian-based approaches and one of those two is characterized by non-binary branching. The possibility of cutting edges is also investigated, being a powerful tool to detect outlier tasks. Moreover, a general concept called Hierarchical Multi-Task Learning Framework is proposed, individually grouping modules, which can be easily extended in future research. Extensive experiments are presented and discussed, showing the potential of employing a hierarchical structure obtained directly from task data within the regularization framework. Both synthetic datasets with known underlying relations among tasks and real-world benchmark datasets from the literature are adopted in the experiments, providing evidence that the proposed framework consistently outperforms well-established multi-task learning strategiesMestradoEngenharia de ComputaçãoMestre em Engenharia ElétricaCAPE

EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS

Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loop—with unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Técnicas de inteligencia artificial aplicadas a sistemas de detección y clasificación de señales de tráfico.

Esta tesis, presentada como conjunto de artículos de investigación, estudia y analiza soluciones para los sistemas de detección y clasificación de señales de tráfico que suponen un reto en aplicaciones de la actualidad, como son la seguridad y asistencia en carretera a conductores, los coches autónomos, el mantenimiento de señalización vertical, o el análisis de escenas de tráfico. Las señales de tráfico constituyen un activo fundamental dentro de la red decarreteras porque su objetivo es ser fácilmente perceptible por los peatones y conductores para advertirles y guiarlos tanto de día como de noche. El hecho de que las señales estén diseñadas para ser únicas y tener características distinguibles, como formas simples y colores uniformes, implica que su detección y reconocimiento sea un problema limitado. Sin embargo, el desarrollo de un sistema de reconocimiento de señales en tiempo real aún presenta desafíos debido a los tiempos de respuesta, los cuales son cruciales para tomar decisiones en el entorno, y la variabilidad que presentan las imágenes de escenas de tráfico, que pueden incluir imágenes a distintas escalas, puntos de vista complicados, oclusiones, y diferentes condiciones de luz. Cualquier sistema de detección y clasificación de señales de tráfico debe hacer frente a estos retos. En este trabajo, se presenta un sistema de clasificación de señales de tráfico basado en aprendizaje profundo (Deep Learning). Concretamente, los principales componentes de la red neuronal profunda (Deep Neural Network) propuesta, son capas convolucionales y redes de transformaciones espaciales (Spatial Transformer Networks). Dicha red es alimentada con imágenes RGB de señales de tráfico de distintos países como Alemania, Bélgica o España. En el caso de las señales de Alemania, que pertenecen al dataset denominado German Traffic Sign Recognition Benchmark (GTSRB), la arquitectura de red y los parámetros de optimización propuestos obtienen un 99.71% de precisión, mejorando tanto al sistema visual humano como a todos los resultados previos del estado del arte, siendo además más eficiente en términos de requisitos de memoria. En el momento de redactar esta tesis, nuestro método se encuentra en la primera posición de la clasificación a nivel mundial. Por otro lado, respecto a la problemática de la detección de señales de tráfico, se analizan varios sistemas de detección de objetos propuestos en el estado del arte, que son específicamente modificados y adaptados al dominio del problema que nos ocupa para aplicar la transferencia de conocimiento en redes neuronales (transfer learning). También se estudian múltiples parámetros de rendimiento para cada uno de los modelos de detección con el fin de ofrecer al lector cuál sería el mejor detector de señales teniendo en cuenta restricciones del entorno donde se desplegará la solución, como la precisión, el consumo de memoria o la velocidad de ejecución. Nuestro estudio muestra que el modelo Faster R-CNN Inception Resnet V2 obtiene la mejor precisión (95.77% mAP), mientras que R-FCN Resnet 101 alcanza el mejor equilibrio entre tiempo de ejecución (85.45 ms por imagen) y precisión (95.15% mAP)

Visual vocabularies for category-level object recognition

This thesis focuses on the study of visual vocabularies for category-level object recognition. Specifically, we state novel approaches for building visual codebooks. Our aim is not just to obtain more discriminative and more compact visual codebooks, but to bridge the gap between visual features and semantic concepts. A novel approach for obtaining class representative visual words is presented. It is based on a maximisation procedure, i. e. the Cluster Precision Maximisation (CPM), of a novel cluster precision criterion, and on an adaptive threshold refinement scheme for agglomerative clustering algorithms based on correlation clustering techniques. The objective is to increase the vocabulary compactness while at the same time improve the recognition rate and further increase the representativeness of the visual words. Moreover, we describe a novel clustering aggregation based approach for building efficient and semantic visual vocabularies. It consist of a novel framework for incorporating neighboring appearances of local descriptors into the vocabulary construction, and a rigorous approach for adding meaningful spatial coherency among the local features into the visual codebooks. We also propose an efficient high-dimensional data clustering algorithm, the Fast Reciprocal Nearest Neighbours (Fast-RNN). Our approach, which is a speeded up version of the standard RNN algorithm, is based on the projection search paradigm. Finally, we release a new database of images called Image Collection of Annotated Real-world Objects (ICARO), which is especially designed for evaluating category-level object recognition systems. An exhaustive comparison of ICARO with other well-known datasets used within the same context is carried out. We also propose a benchmark for both object classification and detection