Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Adaptive Resonance Theory (ART) for social media analytics

This chapter presents the ART-based clustering algorithms for social media analytics in detail. Sections 3.1 and 3.2 introduce Fuzzy ART and its clustering mechanisms, respectively, which provides a deep understanding of the base model that is used and extended for handling the social media clustering challenges. Important concepts such as vigilance region (VR) and its properties are explained and proven. Subsequently, Sects. 3.3-3.7 illustrate five types of ART adaptive resonance theory variants, each of which addresses the challenges in one social media analytical scenario, including automated parameter adaptation, user preference incorporation, short text clustering, heterogeneous data co-clustering and online streaming data indexing. The content of this chapter is several prior studies, including Probabilistic ART [15

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Learning shepherding behavior

Roboter, die Schafe hüten sowie die dazu nötigen Strategien zum Bewegen von Individuen zu einem Ziel, bieten vielseitige Anwendungen wie z. B. die Rettung von Menschen aus bedrohlichen Lagen oder der Einsatz schwimmender Roboter zur Beseitigung von Ölteppichen. In dieser Arbeit nutzen wir ein Multiagentensystem als Modell der Roboter und Schafe. Wir untersuchen die Komplexität des Schafehütens und zeigen einen Greedy-Algorithmus, der in linearer Laufzeit eine fast optimale Lösung berechnet. Weiterhin analysieren wir, wie solche Strategien gelernt werden können, da maschinelles Lernen oftmals vorteilhafte Lösungen findet. Im Folgenden nutzen wir Reinforcement Learning (RL) als Lernmethode. Damit RL Agenten ihr gelerntes Wissen auch in kontinuierlichen oder sehr großen Zustandsräumen (wie im betrachteten Szenario) vorhalten können, sind Methoden zur Wissensabstraktion nötig. Unsere Methoden kombinieren RL mit adaptiven neuronalen Verfahren und erlauben dem Agenten gleichzeitig Strategien sowie Darstellungen dieses Wissens zu lernen. Beide Verfahren basieren auf dem unüberwachten Lernverfahren Growing Neural Gas, das eine Vektorquantisierung lernt, indem es neuronale Einheiten im Eingaberaums platziert und bewegt. GNG-Q gruppiert benachbarte Zustände die gleiches Verhalten erfordern (Zustandsraumapproximation); I-GNG-Q wiederum kombiniert Wissen, um eine glatte Bewertungsfunktion zu erhalten (Approximation der Bewertungsfunktion des RL-Agenten). Beide Verfahren beobachten das Verhalten des Lerners um Stellen der Approximation zu finden, die noch verfeinert werden müssen. Die Hauptvorteile unserer Verfahren sind u.a., dass sie ohne Kenntnis des Modells der Umgebung automatisch eine passende Auflösung der Approximation bestimmen. Die experimentelle Analyse unterstreicht, dass unsere Methoden sehr effiziente und effektive Strategien erzeugen.Artificial shepherding strategies, i.e. using robots to move individuals to given locations, have many applications. For example, people can be guided by mobile robots from dangerous places or swimming robots may help to clean up oil spills. This thesis uses a multiagent system to model the robots and sheep. We analyze the complexity of the shepherding task and present a greedy algorithm that only needs linear time to compute a solution that is proven to be close to optimal. Additionally, we analyze to what extend such strategies can be learned as learning usually provides powerful solutions. This thesis focuses on reinforcement learning (RL) as learning method. To enable RL agents to use their knowledge more efficiently in continuous or large state spaces (as e.g. in the shepherding task), methods to transfer knowledge to unseen but similar situations are required. The approaches developed in this thesis, GNG-Q and I-GNG-Q, combine RL with adaptive neural algorithms and enable the agent to learn behavior in parallel with its representation. Both are based upon the growing neural gas, which is an unsupervised learning approach that learns a vector quantization by placing and adjusting units in the input space. GNG-Q groups states that are spatial close and share the same behavior while I-GNG-Q combines the learned behavior from a larger area of the approximation which results in smoother value functions. Thus, GNG-Q performs a state-space abstraction and I-GNG-Q approximates the value function. Both methods monitor the agent's policy during learning to find regions of the approximation that have to be refined. Amongst many others, the core advantages of our approaches are that they do not need the model of the environment and that the resolution of the approximation is determined automatically. The experimental evaluation underlines that the behaviors learned using our approaches are highly efficient and effective.Michael BaumannTag der Verteidigung: 22.01.2016Fakultät für Elektrotechnik, Informatik und Mathematik, Universität Paderborn, Univ., Dissertation, 201