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Termin (Alle Termine)
Datum Fr 11. Oktober 2019, 11:30 Uhr
Dauer 30 min
Raum Raum 348 (Gebäude 50.34)
Vorheriger Termin Fr 27. September 2019
Nächster Termin Fr 18. Oktober 2019


Vortragende(r) Zdravko Marinov
Titel On the semantics of similarity in deep trajectory representations
Vortragstyp Bachelorarbeit
Betreuer(in) Saeed Taghizadeh
Kurzfassung Recently, a deep learning model (t2vec) for trajectory similarity computation has been proposed. Instead of using the trajectories, it uses their deep representations to compute the similarity between them. At this current state, we do not have a clear idea how to interpret the t2vec similarity values, nor what they are exactly based on. This thesis addresses these two issues by analyzing t2vec on its own and then systematically comparing it to the the more familiar traditional models.

Firstly, we examine how the model’s parameters influence the probability distribution (PDF) of the t2vec similarity values. For this purpose, we conduct experiments with various parameter settings and inspect the abstract shape and statistical properties of their PDF. Secondly, we consider that we already have an intuitive understanding of the classical models, such as Dynamic Time Warping (DTW) and Longest Common Subsequence (LCSS). Therefore, we use this intuition to analyze t2vec by systematically comparing it to DTW and LCSS with the help of heat maps.

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