Fortschritte im unüberwachten Lernen und Anwendungsbereiche: Subspace Clustering mit Hintergrundwissen, semantisches Passworterraten und erlernte Indexstrukturen

Over the past few years, advances in data science, machine learning and, in particular, unsupervised learning have enabled significant progress in many scientific fields and even in everyday life. Unsupervised learning methods are usually successful whenever they can be tailored to specific applications using appropriate requirements based on domain expertise. This dissertation shows how purely theoretical research can lead to circumstances that favor overly optimistic results, and the advantages of application-oriented research based on specific background knowledge. These observations apply to traditional unsupervised learning problems such as clustering, anomaly detection and dimensionality reduction. Therefore, this thesis presents extensions of these classical problems, such as subspace clustering and principal component analysis, as well as several specific applications with relevant interfaces to machine learning. Examples include password guessing using semantic word embeddings and learning spatial index structures using statistical models. In essence, this thesis shows that application-oriented research has many advantages for current and future research.In den letzten Jahren haben Fortschritte in der Data Science, im maschinellen Lernen und insbesondere im unüberwachten Lernen zu erheblichen Fortentwicklungen in vielen Bereichen der Wissenschaft und des täglichen Lebens geführt. Methoden des unüberwachten Lernens sind in der Regel dann erfolgreich, wenn sie durch geeignete, auf Expertenwissen basierende Anforderungen an spezifische Anwendungen angepasst werden können. Diese Dissertation zeigt, wie rein theoretische Forschung zu Umständen führen kann, die allzu optimistische Ergebnisse begünstigen, und welche Vorteile anwendungsorientierte Forschung hat, die auf spezifischem Hintergrundwissen basiert. Diese Beobachtungen gelten für traditionelle unüberwachte Lernprobleme wie Clustering, Anomalieerkennung und Dimensionalitätsreduktion. Daher werden in diesem Beitrag Erweiterungen dieser klassischen Probleme, wie Subspace Clustering und Hauptkomponentenanalyse, sowie einige spezifische Anwendungen mit relevanten Schnittstellen zum maschinellen Lernen vorgestellt. Beispiele sind das Erraten von Passwörtern mit Hilfe semantischer Worteinbettungen und das Lernen von räumlichen Indexstrukturen mit Hilfe statistischer Modelle. Im Wesentlichen zeigt diese Arbeit, dass anwendungsorientierte Forschung viele Vorteile für die aktuelle und zukünftige Forschung hat

CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD

Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier data sets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given data set. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our outlier detection method using coMAD-PCA defines dependent on its variant an inlier region with a robust noise margin by measures of in-distribution (ID) and out-of-distribution (OOD). These measures allow distribution based outlier scoring for each principal component, and thus, for an appropriate alignment of the decision boundary between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), recall and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks

Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study

When researchers publish new cluster algorithms, they usually demonstrate the strengths of their novel approaches by comparing the algorithms' performance with existing competitors. However, such studies are likely to be optimistically biased towards the new algorithms, as the authors have a vested interest in presenting their method as favorably as possible in order to increase their chances of getting published. Therefore, the superior performance of newly introduced cluster algorithms is over-optimistic and might not be confirmed in independent benchmark studies performed by neutral and unbiased authors. This problem is known among many researchers, but so far, the different mechanisms leading to over-optimism in cluster algorithm evaluation have never been systematically studied and discussed. Researchers are thus often not aware of the full extent of the problem. We present an illustrative study to illuminate the mechanisms by which authors-consciously or unconsciously-paint their cluster algorithm's performance in an over-optimistic light. Using the recently published cluster algorithm Rock as an example, we demonstrate how optimization of the used datasets or data characteristics, of the algorithm's parameters and of the choice of the competing cluster algorithms leads to Rock's performance appearing better than it actually is. Our study is thus a cautionary tale that illustrates how easy it can be for researchers to claim apparent superiority of a new cluster algorithm. This illuminates the vital importance of strategies for avoiding the problems of over-optimism (such as, e.g., neutral benchmark studies), which we also discuss in the article