A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.

The revolution in information technology and the explosion in the use of computing devices in people\u27s everyday activities has forever changed the perspective of the data mining and machine learning fields. The enormous amounts of easily accessible, information rich data is pushing the data analysis community in general towards a shift of paradigm. In the new paradigm, data comes in the form a stream of billions of records received everyday. The dynamic nature of the data and its sheer size makes it impossible to use the traditional notion of offline learning where the whole data is accessible at any time point. Moreover, no amount of human resources is enough to get expert feedback on the data. In this work we have developed a unified optimization based learning framework that approaches many of the challenges mentioned earlier. Specifically, we developed a Never-Ending Learning framework which combines incremental/online, semi-supervised, and active learning under a unified optimization framework. The established framework is based on the class of submodular optimization methods. At the core of this work we provide a novel formulation of the Semi-Supervised Support Vector Machines (S3VM) in terms of submodular set functions. The new formulation overcomes the non-convexity issues of the S3VM and provides a state of the art solution that is orders of magnitude faster than the cutting edge algorithms in the literature. Next, we provide a stream summarization technique via exemplar selection. This technique makes it possible to keep a fixed size exemplar representation of a data stream that can be used by any label propagation based semi-supervised learning technique. The compact data steam representation allows a wide range of algorithms to be extended to incremental/online learning scenario. Under the same optimization framework, we provide an active learning algorithm that constitute the feedback between the learning machine and an oracle. Finally, the developed Never-Ending Learning framework is essentially transductive in nature. Therefore, our last contribution is an inductive incremental learning technique for incremental training of SVM using the properties of local kernels. We demonstrated through this work the importance and wide applicability of the proposed methodologies

A monitoring and threat detection system using stream processing as a virtual function for big data

The late detection of security threats causes a significant increase in the risk of irreparable damages, disabling any defense attempt. As a consequence, fast realtime threat detection is mandatory for security guarantees. In addition, Network Function Virtualization (NFV) provides new opportunities for efficient and low-cost security solutions. We propose a fast and efficient threat detection system based on stream processing and machine learning algorithms. The main contributions of this work are i) a novel monitoring threat detection system based on stream processing; ii) two datasets, first a dataset of synthetic security data containing both legitimate and malicious traffic, and the second, a week of real traffic of a telecommunications operator in Rio de Janeiro, Brazil; iii) a data pre-processing algorithm, a normalizing algorithm and an algorithm for fast feature selection based on the correlation between variables; iv) a virtualized network function in an open-source platform for providing a real-time threat detection service; v) near-optimal placement of sensors through a proposed heuristic for strategically positioning sensors in the network infrastructure, with a minimum number of sensors; and, finally, vi) a greedy algorithm that allocates on demand a sequence of virtual network functions.A detecção tardia de ameaças de segurança causa um significante aumento no risco de danos irreparáveis, impossibilitando qualquer tentativa de defesa. Como consequência, a detecção rápida de ameaças em tempo real é essencial para a administração de segurança. Além disso, A tecnologia de virtualização de funções de rede (Network Function Virtualization - NFV) oferece novas oportunidades para soluções de segurança eficazes e de baixo custo. Propomos um sistema de detecção de ameaças rápido e eficiente, baseado em algoritmos de processamento de fluxo e de aprendizado de máquina. As principais contribuições deste trabalho são: i) um novo sistema de monitoramento e detecção de ameaças baseado no processamento de fluxo; ii) dois conjuntos de dados, o primeiro ´e um conjunto de dados sintético de segurança contendo tráfego suspeito e malicioso, e o segundo corresponde a uma semana de tráfego real de um operador de telecomunicações no Rio de Janeiro, Brasil; iii) um algoritmo de pré-processamento de dados composto por um algoritmo de normalização e um algoritmo para seleção rápida de características com base na correlação entre variáveis; iv) uma função de rede virtualizada em uma plataforma de código aberto para fornecer um serviço de detecção de ameaças em tempo real; v) posicionamento quase perfeito de sensores através de uma heurística proposta para posicionamento estratégico de sensores na infraestrutura de rede, com um número mínimo de sensores; e, finalmente, vi) um algoritmo guloso que aloca sob demanda uma sequencia de funções de rede virtual

Towards solving computer vision problems: datasets, labels, algorithms, and applications

The solution to a supervised computer vision problem consists of an application, algorithm, input data, and a set of human generated labels. Solving these kinds of tasks involves collecting large quantities of data, collecting appropriate labels, and developing machine vision algorithms tailored to the application. Progress on these problems has often benefited from large scale datasets with high fidelity labels. Successful algorithms display a synergy between application goals and the size and quality of the dataset. This thesis presents work highlighting the importance of each component of a supervised vision task.First, the problem of automatically classifying groups of people into social categories is introduced. This problem is called Urban Tribe Classification. To tackle this problem, each individual and the entire group of individuals are modeled. Since this was a newly introduced computer vision problem, a dataset for this task was created. On this dataset, the combined representation of group and individuals outperforms using only the person representations. This model showed promising results for automatic subculture classification.Second, the problem of creating perceptual embeddings based on human similarity judgements is tackled. This work focuses on triplet similarity comparisons of the form ``Is object $i$ more similar to $j$ or $k$?'', which have been useful for computer vision and machine learning applications. Unfortunately, triplet similarity comparisons, like many human labeling efforts, can be prohibitively expensive. This work proposes two techniques for dealing with this obstacle. First, an alternative display for collecting triplets is designed. This display shows a probe image and a grid of query images, allowing the user to collect multiple triplets simultaneously. The display is shown to reduce the cost and time of triplet collection. In addition, higher quality embeddings are created with the improved triplet collection UI. A 10,000-food item dataset of human taste similarity was created using this UI. Second, ``SNaCK,'' a low-dimensional perceptual embedding algorithm that combines human expertise with automatic machine kernels, is introduced. Both parts are complementary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. Finally, the precise localization of key frames of an action is explored. This work focuses on detecting the exact starting frame of a behavior, an important task for neuroscience research. To address this problem, a loss designed to penalize extra and missed action start detections over small misalignments. Recurrent neural networks (RNN) are trained to optimize this loss. The model is shown to reduce the number of false positives, an important criteria defined by the neuroscientist. The performance of the model is evaluated on a new dataset, the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was created for neuroscience research. On this dataset, the proposed model outperforms related approaches and baseline methods using an unstructured loss

Mutually Adaptive Distributed Heterogeneous Agents for Data Classification

Efficient and effective data classification is one of the most fundamental problems of computer science due to a huge number of important applications. As such, there have been many models that are capable of doing some form of data classification using a large number of varied algorithms and architectures. However, few share properties that implicitly take advantage of the distributed environment where classification has its most potential. Therefore, to resolve this dilemma in this thesis a generic classification architecture com posed of mutually adaptive distributed heterogeneous agents was designed, implemented and experimentally analyzed. The system defined creates a multi-agent distributed ar chitecture whereby individual agents cooperate and collaborate with each other to autonomously perform classification on arbitrary data using a hierarchical paradigm. The main contributions of this work are the following: agents follow hierarchical pro cesses to form an organization which can autonomously train, test, assign and evaluate work of other agents. Through experimental analysis it was found that this design improves the classification accuracy relative to previously implemented classification algorithms and architectures. Furthermore this analysis shows that the generated architecture will au tonomously adapt to previously unlearned data and responds to failures with little or no degradation of classification quality

Improving Representation Learning for Deep Clustering and Few-shot Learning

The amounts of data in the world have increased dramatically in recent years, and it is quickly becoming infeasible for humans to label all these data. It is therefore crucial that modern machine learning systems can operate with few or no labels. The introduction of deep learning and deep neural networks has led to impressive advancements in several areas of machine learning. These advancements are largely due to the unprecedented ability of deep neural networks to learn powerful representations from a wide range of complex input signals. This ability is especially important when labeled data is limited, as the absence of a strong supervisory signal forces models to rely more on intrinsic properties of the data and its representations. This thesis focuses on two key concepts in deep learning with few or no labels. First, we aim to improve representation quality in deep clustering - both for single-view and multi-view data. Current models for deep clustering face challenges related to properly representing semantic similarities, which is crucial for the models to discover meaningful clusterings. This is especially challenging with multi-view data, since the information required for successful clustering might be scattered across many views. Second, we focus on few-shot learning, and how geometrical properties of representations influence few-shot classification performance. We find that a large number of recent methods for few-shot learning embed representations on the hypersphere. Hence, we seek to understand what makes the hypersphere a particularly suitable embedding space for few-shot learning. Our work on single-view deep clustering addresses the susceptibility of deep clustering models to find trivial solutions with non-meaningful representations. To address this issue, we present a new auxiliary objective that - when compared to the popular autoencoder-based approach - better aligns with the main clustering objective, resulting in improved clustering performance. Similarly, our work on multi-view clustering focuses on how representations can be learned from multi-view data, in order to make the representations suitable for the clustering objective. Where recent methods for deep multi-view clustering have focused on aligning view-specific representations, we find that this alignment procedure might actually be detrimental to representation quality. We investigate the effects of representation alignment, and provide novel insights on when alignment is beneficial, and when it is not. Based on our findings, we present several new methods for deep multi-view clustering - both alignment and non-alignment-based - that out-perform current state-of-the-art methods. Our first work on few-shot learning aims to tackle the hubness problem, which has been shown to have negative effects on few-shot classification performance. To this end, we present two new methods to embed representations on the hypersphere for few-shot learning. Further, we provide both theoretical and experimental evidence indicating that embedding representations as uniformly as possible on the hypersphere reduces hubness, and improves classification accuracy. Furthermore, based on our findings on hyperspherical embeddings for few-shot learning, we seek to improve the understanding of representation norms. In particular, we ask what type of information the norm carries, and why it is often beneficial to discard the norm in classification models. We answer this question by presenting a novel hypothesis on the relationship between representation norm and the number of a certain class of objects in the image. We then analyze our hypothesis both theoretically and experimentally, presenting promising results that corroborate the hypothesis