Ultrametric Component Analysis with Application to Analysis of Text and of Emotion

We review the theory and practice of determining what parts of a data set are ultrametric. It is assumed that the data set, to begin with, is endowed with a metric, and we include discussion of how this can be brought about if a dissimilarity, only, holds. The basis for part of the metric-endowed data set being ultrametric is to consider triplets of the observables (vectors). We develop a novel consensus of hierarchical clusterings. We do this in order to have a framework (including visualization and supporting interpretation) for the parts of the data that are determined to be ultrametric. Furthermore a major objective is to determine locally ultrametric relationships as opposed to non-local ultrametric relationships. As part of this work, we also study a particular property of our ultrametricity coefficient, namely, it being a function of the difference of angles of the base angles of the isosceles triangle. This work is completed by a review of related work, on consensus hierarchies, and of a major new application, namely quantifying and interpreting the emotional content of narrative.Comment: 49 pages, 15 figures, 52 citation

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Abstract- There have been several attempts to model emotions in airtanomous agents and robotics. The use of emotions in conjunction with reinforcement learning in particular has arnacted attention since both notions are borrowed analogies from psychology The work presented here is an approach to robot control based on modeling emotions within reinforcement learning algorithm. The main cantribidion ofthis paper is the use offizzy cognitive mops (FCMj to facilitate the modeling of emotions and inferencing for action selection, This approach does nor use feeling estimation; instead a direct link between sensoiy data and emotions is used for emotional estimation. An emotion based reinforcement learning algarithm is proposed for action selection in robotic control

The

Image segmentation is the most important step in modern computer vision. Its output is crucial for all the other stages of computer vision. The literature is very rich in segmentation techniques and neural-network based methods have been applied successfully due to their signal-to-noise independency, their ability to achieve real-time results and the ease of implementing them with massive VLSI processors