Language Model Co-occurrence Linking for Interleaved Activity Discovery

Eoin Rogers, John D. Kelleher, Robert J. Ross

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from previous work in that it explicitly aims to deal with interleaving (switching back and forth between activities) in a principled manner, by utilising the long-term memory capabilities of a recurrent neural network cell. We present our approach and test it on a realistic dataset to evaluate its performance. Our results show the viability of the approach and that it shows promise for further investigation. We believe this is a useful direction to consider in accounting for the continually changing nature of behaviours.

Original languageEnglish
Title of host publicationMachine Learning for Networking - 2nd IFIP TC 6 International Conference, MLN 2019, Revised Selected Papers
EditorsSelma Boumerdassi, Éric Renault, Paul Mühlethaler
PublisherSpringer
Pages70-84
Number of pages15
ISBN (Print)9783030457778
DOIs
Publication statusPublished - 2020
Event2nd International Conference on Machine Learning for Networking, MLN 2019 - Paris, France
Duration: 3 Dec 20195 Dec 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12081 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Machine Learning for Networking, MLN 2019
Country/TerritoryFrance
CityParis
Period3/12/195/12/19

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