Abstract
Bayesian nonparametric (BNP) models have recently become popular due to their flexibility in identifying the unknown number of clusters. However, they have difficulties handling heterogeneous data from multiple sources. Existing BNP methods either treat each of these sources independently - hence do not get benefits from the correlating information between them, or require to explicitly specify data sources as primary and context channels. In this paper, we present a BNP framework, termed MCNC, which has the ability to (1) discover co-patterns from multiple sources; (2) explore multi-channel data simultaneously and treat them equally; (3) automatically identify a suitable number of patterns from data; and (4) handle missing data. The key idea is to utilize a richer base measure of a BNP model being a product-space. We demonstrate our framework on synthetic and real-world datasets to discover the identity-location-time (a.k.a who-where-when) patterns. The experimental results highlight the effectiveness of our MCNC framework in both cases of complete and missing data.
Original language | English |
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Title of host publication | 2016 23rd International Conference on Pattern Recognition (ICPR 2016) |
Editors | Larry Davis , Alberto Del Bimbo, Brian C. Lovell |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 3633-3638 |
Number of pages | 6 |
ISBN (Electronic) | 9781509048472 |
ISBN (Print) | 9781509048489 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | International Conference on Pattern Recognition 2016 - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016 Conference number: 23rd http://www.icpr2016.org/site/ https://ieeexplore.ieee.org/xpl/conhome/7893644/proceeding (Proceedings) |
Conference
Conference | International Conference on Pattern Recognition 2016 |
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Abbreviated title | ICPR 2016 |
Country/Territory | Mexico |
City | Cancun |
Period | 4/12/16 → 8/12/16 |
Internet address |
Keywords
- Bayesian nonparametrics
- Clustering
- Heterogeneous data
- MCNC
- Product-space
- Ubiquitous computing