Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning

This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its implementation in Apache Spark further increases its ability to handle very large datasets. Moreover, the algorithm incorporates an automatic hyperparameter tuning mechanism so that users do not have to implement costly manual tuning. Our LSHAD method is novel as both hyperparameter automation and distributed properties are not usual in AD techniques. Our results for experiments with LSHAD across a variety of datasets point to state-of-the-art AD performance while handling much larger datasets than state-of-the-art alternatives. In addition, evaluation results for the tradeoff between AD performance and scalability show that our method offers significant advantages over competing methods.This research has been financially supported in part by the Spanish Ministerio de Economía y Competitividad (project PID-2019-109238GB-C22) and by the Xunta de Galicia (grants ED431C 2018/34 and ED431G 2019/01) through European Union ERDF funds. CITIC, as a research center accredited by the Galician University System, is funded by the Consellería de Cultura, Educación e Universidades of the Xunta de Galicia, supported 80% through ERDF Funds (ERDF Operational Programme Galicia 2014–2020) and 20% by the Secretaría Xeral de Universidades (Grant ED431G 2019/01).This work was also supported by National Funds through the Portuguese FCT - Fundação para a Ciência e a Tecnologia (projects UIDB/00760/2020 and UIDP/00760/2020).info:eu-repo/semantics/publishedVersio

Deep Binary Representation Learning for Single/Cross-Modal Data Retrieval

Data similarity search is widely regarded as a classic topic in the realms of computer vision, machine learning and data mining. Providing a certain query, the retrieval model sorts out the related candidates in the database according to their similarities, where representation learning methods and nearest-neighbour search apply. As matching data features in Hamming space is computationally cheaper than in Euclidean space, learning to hash and binary representations are generally appreciated in modern retrieval models. Recent research seeks solutions in deep learning to formulate the hash functions, showing great potential in retrieval performance. In this thesis, we gradually extend our research topics and contributions from unsupervised single-modal deep hashing to supervised cross-modal hashing _nally zero-shot hashing problems, addressing the following challenges in deep hashing. First of all, existing unsupervised deep hashing works are still not attaining leading retrieval performance compared with the shallow ones. To improve this, a novel unsupervised single-modal hashing model is proposed in this thesis, named Deep Variational Binaries (DVB). We introduce the popular conditional variational auto-encoders to formulate the encoding function. By minimizing the reconstruction error of the latent variables, the proposed model produces compact binary codes without training supervision. Experiments on benchmarked datasets show that our model outperform existing unsupervised hashing methods. The second problem is that current cross-modal hashing methods only consider holistic image representations and fail to model descriptive sentences, which is inappropriate to handle the rich semantics of informative cross-modal data for quality textual-visual search tasks. To handle this problem, we propose a supervised deep cross-modal hashing model called Textual-Visual Deep Binaries (TVDB). Region-based neural networks and recurrent neural networks are involved in the image encoding network in order to make e_ective use of visual information, while the text encoder is built using a convolutional neural network. We additionally introduce an e_cient in-batch optimization routine to train the network parameters. The proposed mode successfully outperforms state-of-the-art methods on large-scale datasets. Finally, existing hashing models fail when the categories of query data have never been seen during training. This scenario is further extended into a novel zero-shot cross-modal hashing task in this thesis, and a Zero-shot Sketch-Image Hashing (ZSIH) scheme is then proposed with graph convolution and stochastic neurons. Experiments show that the proposed ZSIH model signi_cantly outperforms existing hashing algorithms in the zero-shot retrieval task. Experiments suggest our proposed and novel hashing methods outperform state-of-the-art researches in single-modal and cross-modal data retrieval

Machine learning and privacy preserving algorithms for spatial and temporal sensing

Sensing physical and social environments are ubiquitous in modern mobile phones, IoT devices, and infrastructure-based settings. Information engraved in such data, especially the time and location attributes have unprecedented potential to characterize individual and crowd behaviour, natural and technological processes. However, it is challenging to extract abstract knowledge from the data due to its massive size, sequential structure, asynchronous operation, noisy characteristics, privacy concerns, and real time analysis requirements. Therefore, the primary goal of this thesis is to propose theoretically grounded and practically useful algorithms to learn from location and time stamps in sensor data. The proposed methods are inspired by tools from geometry, topology, and statistics. They leverage structures in the temporal and spatial data by probabilistically modeling noise, exploring topological structures embedded, and utilizing statistical structure to protect personal information and simultaneously learn aggregate information. Proposed algorithms are geared towards streaming and distributed operation for efficiency. The usefulness of the methods is argued using mathematical analysis and empirical experiments on real and artificial datasets

Privacy-aware publication and utilization of healthcare data

textOpen access to health data can bring enormous social and economical benefits. However, such access can also lead to privacy breaches, which may result in discrimination in insurance and employment markets. Privacy is a subjective and contextual concept, thus it should be interpreted from both systemic and information perspectives to clearly understand potential breaches and consequences. This dissertation investigates three popular use cases of healthcare data: specifically, 1) synthetic data publication, 2) aggregate data utilization, and 3) privacy-aware API implementation. For each case, we develop statistical models that improve the privacy-utility Pareto frontier by leveraging a variety of machine learning techniques such as information theoretic privacy measures, Bayesian graphical models, non-parametric modeling, and low-rank factorization techniques. It shows that much utility can be extracted from health records while maintaining strong privacy guarantees and protection of sensitive health information.Electrical and Computer Engineerin

Soundtrack recommendation for images

The drastic increase in production of multimedia content has emphasized the research concerning its organization and retrieval. In this thesis, we address the problem of music retrieval when a set of images is given as input query, i.e., the problem of soundtrack recommendation for images. The task at hand is to recommend appropriate music to be played during the presentation of a given set of query images. To tackle this problem, we formulate a hypothesis that the knowledge appropriate for the task is contained in publicly available contemporary movies. Our approach, Picasso, employs similarity search techniques inside the image and music domains, harvesting movies to form a link between the domains. To achieve a fair and unbiased comparison between different soundtrack recommendation approaches, we proposed an evaluation benchmark. The evaluation results are reported for Picasso and the baseline approach, using the proposed benchmark. We further address two efficiency aspects that arise from the Picasso approach. First, we investigate the problem of processing top-K queries with set-defined selections and propose an index structure that aims at minimizing the query answering latency. Second, we address the problem of similarity search in high-dimensional spaces and propose two enhancements to the Locality Sensitive Hashing (LSH) scheme. We also investigate the prospects of a distributed similarity search algorithm based on LSH using the MapReduce framework. Finally, we give an overview of the PicasSound|a smartphone application based on the Picasso approach.Der drastische Anstieg von verfügbaren Multimedia-Inhalten hat die Bedeutung der Forschung über deren Organisation sowie Suche innerhalb der Daten hervorgehoben. In dieser Doktorarbeit betrachten wir das Problem der Suche nach geeigneten Musikstücken als Hintergrundmusik für Diashows. Wir formulieren die Hypothese, dass die für das Problem erforderlichen Kenntnisse in öffentlich zugänglichen, zeitgenössischen Filmen enthalten sind. Unser Ansatz, Picasso, verwendet Techniken aus dem Bereich der Ähnlichkeitssuche innerhalb von Bild- und Musik-Domains, um basierend auf Filmszenen eine Verbindung zwischen beliebigen Bildern und Musikstücken zu lernen. Um einen fairen und unvoreingenommenen Vergleich zwischen verschiedenen Ansätzen zur Musikempfehlung zu erreichen, schlagen wir einen Bewertungs-Benchmark vor. Die Ergebnisse der Auswertung werden, anhand des vorgeschlagenen Benchmarks, für Picasso und einen weiteren, auf Emotionen basierenden Ansatz, vorgestellt. Zusätzlich behandeln wir zwei Effizienzaspekte, die sich aus dem Picasso Ansatz ergeben. (i) Wir untersuchen das Problem der Ausführung von top-K Anfragen, bei denen die Ergebnismenge ad-hoc auf eine kleine Teilmenge des gesamten Indexes eingeschränkt wird. (ii) Wir behandeln das Problem der Ähnlichkeitssuche in hochdimensionalen Räumen und schlagen zwei Erweiterungen des Lokalitätssensitiven Hashing (LSH) Schemas vor. Zusätzlich untersuchen wir die Erfolgsaussichten eines verteilten Algorithmus für die Ähnlichkeitssuche, der auf LSH unter Verwendung des MapReduce Frameworks basiert. Neben den vorgenannten wissenschaftlichen Ergebnissen beschreiben wir ferner das Design und die Implementierung von PicassSound, einer auf Picasso basierenden Smartphone-Anwendung

Distributed multi-label learning on Apache Spark

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art