Classifiability-based Optimal Discriminatory Projection Pursuit

Linear Discriminant Analysis (LDA) might be the most widely used linear feature extraction method in pattern recognition. Based on the analysis on the several limitations of traditional LDA, this paper makes an effort to propose a new computational paradigm named Optimal Discriminatory Projection Pursuit (ODPP), which is totally different from the traditional LDA and its variants. Only two simple steps are involved in the proposed ODPP: one is the construction of candidate projection set; the other is the optimal discriminatory projection pursuit. For the former step, candidate projections are generated as the difference vectors between nearest between-class boundary samples with redundancy well-controlled, while the latter is efficiently achieved by classifiability-based AdaBoost learning from the large candidate projection set. We show that the new 'projection pursuit' paradigm not only does not suffer from the limitations of the traditional LDA but also inherits good generalizability from the boundary attribute of candidate projections. Extensive experimental comparisons with LDA and its variants on synthetic and real data sets show that the proposed method consistently has better performances. ?2008 IEEE.EI

Latent gaze information in highly dynamic decision-tasks

Die Digitalisierung durchdringt immer mehr Lebensbereiche. Aufgaben werden zunehmend digital erledigt und damit schneller, effizienter, aber auch zielorientierter und erfolgreicher erfüllt. Die rasante Entwicklung im Bereich der künstlichen Intelligenz in den letzten Jahren hat dabei eine große Rolle gespielt, denn sie hat viele hilfreiche Ansätze hervorgebracht, auf die immer weiter aufgebaut werden kann. Gleichzeitig werden die Augen, ihre Bewegungen und die Bedeutung dieser Bewegungen immer weiter erforscht. Die Verknüpfung dieser Entwicklungen hat zu spannenden Ansätzen in der Wissenschaft geführt. In dieser Dissertation stelle ich einige der Ansätze vor, an denen ich während meiner Promotion gearbeitet habe. Zunächst gebe ich einen Einblick in die Entwicklung von Modellen, die mit Hilfe künstlicher Intelligenz Verbindungen zwischen Augenbewegungsdaten und visueller Expertise herstellen. Dies wird anhand zwei verschiedener Bereiche, genauer gesagt zwei verschiedener Personengruppen, demonstriert: Sportler bei Entscheidungsfindungen und Chirurgen bei arthroskopischen Eingriffen. Die daraus resultierenden Modelle können als digitale Diagnosemodelle für die automatische Erkennung von visueller Expertise betrachtet werden. Darüber hinaus stelle ich Ansätze vor, die die Übertragbarkeit von Augenbewegungsmustern auf verschiedene Kompetenzbereiche untersuchen sowie wichtige Aspekte von Techniken zur Generalisierung. Schließlich befasse ich mich mit der zeitlichen Erkennung von Verwirrung auf der Grundlage von Augenbewegungsdaten. Die Ergebnisse legen eine Nutzung der Modelle als Zeitgeber für mögliche digitale Assistenzoptionen in der Ausbildung von Berufsanfängern nahe. Eine Besonderheit meiner Untersuchungen besteht darin, dass ich auf sehr wervolle Daten von DFB-Jugendkaderathleten sowie von langjährigen Experten in der Arthroskopie zurückgreifen konnte. Insbesondere die Arbeit mit den DFB-Daten stieß auf das Interesse von Radiound Printmedien, genauer, DeutschlandFunk Nova und SWR DasDing. Alle hier vorgestellten Beiträge wurden in international renommierten Fachzeitschriften oder auf Konferenzen veröffentlicht.Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences

Techniques for data pattern selection and abstraction

This thesis concerns the problem of prototype reduction in instance-based learning. In order to deal with problems such as storage requirements, sensitivity to noise and computational complexity, various algorithms have been presented that condense the number of stored prototypes, while maintaining competent classification accuracy. Instance selection, which recovers a smaller subset of the original training set, is the most widely used technique for instance reduction. But, prototype abstraction that generates new prototypes to replace the initial ones has also gained a lot of interest recently. The major contribution of this work is the proposal of four novel frameworks for performing prototype reduction, the Class Boundary Preserving algorithm (CBP), a hybrid method that uses both selection and generation of prototypes, Instance Seriation for Prototype Abstraction (ISPA), which is an abstraction algorithm, and two selective techniques, Spectral Instance Reduction (SIR) and Direct Weight Optimization (DWO). CBP is a multi-stage method based on a simple heuristic that is very effective in identifying samples close to class borders. Using a noise filter harmful instances are removed, while the powerful heuristic determines the geometrical distribution of patterns around every instance. Together with the concepts of nearest enemy pairs and mean shift clustering this algorithm decides on the final set of retained prototypes. DWO is a selection model whose output set of prototypes is decided by a set of binary weights. These weights are computed according to an objective function composed of the ratio between the nearest friend and nearest enemy of every sample. In order to obtain good quality results DWO is optimized using a genetic algorithm. ISPA is an abstraction technique that employs the concept of data seriation to organize instances in an arrangement that favours merging between them. As a result, a new set of prototypes is created. Results show that CBP, SIR and DWO, the three major algorithms presented in this thesis, are competent and efficient in terms of at least one of the two basic objectives, classification accuracy and condensation ratio. The comparison against other successful condensation algorithms illustrates the competitiveness of the proposed models. The SIR algorithm presents a set of border discriminating features (BDFs) that depicts the local distribution of friends and enemies of all samples. These are then used along with spectral graph theory to partition the training set in to border and internal instances