Inference of Effective Pairwise Relations for Data Processing
In the first part of the thesis, we consider Markov chains, noting that pairwise relations between its states are naturally modeled by the state-transition matrix. We propose a method for modeling the performance of a synchronization method for a multi-processor architecture. Our model introduces and builds upon a cache line bouncing process that models the interaction of threads accessing the shared cache lines.
In the second part of the thesis, we consider representation learning using the transitive-aware Minimax distance, which enables the extraction of elongated manifolds and structures in the data. While recent work has made Minimax distances computationally feasible, little attention has been put to its memory footprint, which is naturally O(N^2), the cost of storing all pairwise distances. We do, however, compute a novel hierarchical representation of the data, requiring O(N) memory, from which pairwise Minimax distances can then be efficiently inferred, in total requiring O(N) memory, at the cost of higher computational cost.
An alternative sampling-based approach is also derived, which computes approximate Minimax distances, also in O(N) memory but with a significantly reduced computational cost, while still yielding a good approximation, as verified by impressive results on clustering benchmarks.
Finally, we develop an unsupervised learning framework for clustering vehicle trajectories based on Minimax distances. The performance of the framework is validated on real-world datasets collected from real driving scenarios, on which satisfactory performance is demonstrated.
Motion trajectory clustering
Fazeleh Sadat Hoseini
Chalmers, Data- och informationsteknik, Nätverk och system
Modeling the performance of atomic primitives on modern architectures
ACM International Conference Proceeding Series,; (2019)
Paper i proceeding
Hoseini, Fazeleh Sadat, and Morteza Haghir Chehreghani. "Memory-Efficient Sampling for Minimax Distance Measures." arXiv preprint arXiv:2005.12627 (2020).
Hoseini, Fazeleh S., Sadegh Rahrovani, and Morteza Haghir Chehreghani. "A Generic Framework for Clustering Vehicle Motion Trajectories." arXiv preprint arXiv:2009.12443 (2020).
Annan data- och informationsvetenskap
Informations- och kommunikationsteknik
C3SE (Chalmers Centre for Computational Science and Engineering)
Chalmers tekniska högskola
CSE EDIT 8103
Opponent: Prof. Niklas Lavesson, Department of Computer Science and Informatics, Jönköping University of Technology