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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gupta, S.K. Dinh Phung Venkatesh, S. |
| Copyright Year | 2012 |
| Description | Author affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Australia (Gupta, S.K.; Dinh Phung; Venkatesh, S.) |
| Abstract | We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models. |
| Starting Page | 1815 |
| Ending Page | 1818 |
| File Size | 370507 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467322164 |
| ISSN | 10514651 |
| e-ISBN | 9784990644109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-11 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | ICPR Org Committee |
| Subject Keyword | Data models Analytical models Bayesian methods Dictionaries Face Load modeling Indexes |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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