On the semantics of similarity in deep trajectory representations: Unterschied zwischen den Versionen

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|vortragender=Zdravko Marinov
|vortragender=Zdravko Marinov
|email=ufijd@student.kit.edu
|email=ufijd@student.kit.edu
|vortragstyp=Proposal
|vortragstyp=Bachelorarbeit
|betreuer=Saeed Taghizadeh
|betreuer=Saeed Taghizadeh
|termin=Institutsseminar/2019-05-31
|termin=Institutsseminar/2019-10-11
|kurzfassung=There are many approaches to compute the similarity between two trajectories. In 2018 a novel approach using deep learning had been introduced, which has several advantages against the classical models like Dynamic Time Warping, Edit Distance with Real Penalty etc. In this thesis the influence of the parameters of the deep learning approach considering the similarity are investigated. The similarity values are further compared between the novel and the classical models and in the end the quality of the similarity computation is evaluated via trajectory clustering techniques​ and finding popular routes in cities.
|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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Aktuelle Version vom 30. September 2019, 10:16 Uhr

Vortragende(r) Zdravko Marinov
Vortragstyp Bachelorarbeit
Betreuer(in) Saeed Taghizadeh
Termin Fr 11. Oktober 2019
Vortragsmodus
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.