Computable social patterns from sparse sensor data

ACM International Conference Proceeding Series

Volumn 300, Issue , 2008, Pages 69-72

  Phung, Dinh ^a   Adams, Brett ^a   Venkatesh, Svetha ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

latent Dirichlet allocation; social footprints; social pattern

Indexed keywords

BAYESIAN CLUSTERING; CODEBOOKS; COMPUTATIONAL FRAMEWORK; DAILY ACTIVITY; DATA SETS; DIMENSIONALITY REDUCTION METHOD; DIRICHLET; DOCUMENT ANALYSIS; HIGH-ORDER; LATENT DIRICHLET ALLOCATION; LOCATION DATA; MODELING TOOL; RAW SENSOR; REAL-WORLD; SENSOR DATA; SOCIAL ACTIVITIES; SOCIAL PATTERNS; STATISTICAL STRENGTH; TIME PERIODS; TOPIC MODEL;

SENSORS;

METADATA;

EID: 77954437136     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1367798.1367810     Document Type: Conference Paper

References (6)

1. B. Adams, D. Phung, and S. Venkatesh. Sensing and using social context. ACM Transaction on Multimedia Computing, Communications and Applications, 2008. to appear.
2. D.M. Blei, A.Y. Ng, and M.I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3(5):993-1022, 2003.
3. N. Eagle and A. Pentland. Eigenbehaviors: Identifying Structure in Routine, October 2005. Technical report, Human Dynamics Lab, Massachusetts Institute of Technology (MIT), 2005.
4. R. Hariharan and K. Toyama. Project lachesis: Parsing and modeling location histories. Lecture Notes in Computer Science, 3234:106-124, 2004.
5. C.D. Manning and H. Sch'eutze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.
6. Xuerui Wang, Andrew McCallum, and Xing Wei. Topical n-grams: Phrase and topic discovery, with an application to information retrieval. In Proceedings of the 7th IEEE International Conference on Data Mining, pages 697-702, 2007