Name
Abstract | ||
|---|---|---|
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn from a matrix-variate t distribution and suggest a matrix- variate t model (MVTM) to predict those missing elements. We show that MVTM generalizes a range of known probabilistic models, and automatically performs model selection to encourage sparse predictive models. Due to the non-conjugacy of its prior, it is difficult to make predictions by computing the mode or mean of the posterior distribution. We suggest an optimization method that sequentially minimizes a convex upper-bound of the log-likelihood, which is very efficient and scalable. The experiments on a toy data and EachMovie dataset show a good predictive accuracy of the model. |
Year | Venue | Keywords |
|---|---|---|
2007 | NIPS | random matrix,posterior distribution,prediction model,probabilistic model,upper bound |
Field | DocType | Citations |
Random variate,T-model,Computer science,Matrix (mathematics),Mode (statistics),Model selection,Posterior probability,Artificial intelligence,Probabilistic logic,Machine learning,Random matrix | Conference | 8 |
PageRank | References | Authors |
0.74 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhu, Shenghuo | 1 | 2996 | 167.68 |
| Yu, Kai | 2 | 4799 | 255.21 |
| yihong gong | 3 | 7300 | 470.57 |