Discovery of activity composites using topic models: An analysis of unsupervised methods

Pervasive and Mobile Computing

Volumn 15, Issue , 2014, Pages 215-227

  Seiter, Julia ^a   Amft, Oliver ^b   Rossi, Mirco ^a   Tröster, Gerhard ^a  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF PASSAU   (Germany)

Author keywords

Activity discovery; Activity routines; Daily routines; Hierarchical activity recognition; Topic modeling

Indexed keywords

STATISTICS;

ACTIVITY DISCOVERIES; ACTIVITY RECOGNITION; DAILY ROUTINES; K - MEANS CLUSTERING; LATENT DIRICHLET ALLOCATIONS; PARAMETER SELECTION; ROBUSTNESS AGAINST NOISE; TOPIC MODELING;

DATA MINING;

EID: 84912150479     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2014.05.007     Document Type: Article

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