The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of his/her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the mailing list webpage. Anyone can attend the seminars; no registration is necessary. If you would like to present something, please send us an email.

The seminars are usually held every other Tuesday, from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Tuesday, May 30th 2017, 13h00 - 14h00

Telmo Pires (IT/Unbabel)

Shape-Based Trajectory Clustering

Anfiteatro do Complexo Interdisciplinar

Instituto Superior Técnico - Alameda


Automatic trajectory classification has countless applications, ranging from the natural sciences, such as zoology and meteorology, to urban planning and sports analysis, and has generated great interest and investigation. The purpose of this work is to propose and test new methods for trajectory clustering, based on shape, rather than spatial position, as is the case with previous methods. The proposed approach starts by uniformly resampling the trajectories using splines, and then characterizes them using the angles of the tangents at the resampled points. Angular data introduces some challenges for analysis, due to its periodic nature, therefore preventing the direct application of common clustering techniques. To overcome this problem, three methods are proposed/adapted: a variant of the k-means algorithm, a mixture model using multivariate Von Mises distributions, which is fitted using the EM algorithm, and sparse nonnegative matrix factorization. Since the number of clusters is seldom known a priori, methods for automatic model selection are also introduced. Finally, these techniques are tested on both real and synthetic data, and the viability of this approach is demonstrated.


Bio: Telmo Pires is a researcher at IT/Unbabel, where he is working in neural architectures for machine translation. His interests include machine learning, artificial intelligence and entrepreneurship, and he holds a MSc in Aerospace Engineering from Instituto Superior Técnico (University of Lisbon, 2016).

Eventbrite - Shape-Based Trajectory Clustering