MAP Estimation With Bernoulli Randomness, and Its Application to Text Analysis and Recommender Systems
| Autor: | Khoat Than, Hieu D. Vu, Oanh T. H. Nguyen, Xuan Bui |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
probabilistic models Computer science General Engineering Probabilistic logic 02 engineering and technology Overfitting Recommender system Regularization (mathematics) Bernoulli's principle online MAP estimation Rate of convergence 020204 information systems 0202 electrical engineering electronic engineering information engineering Maximum a posteriori estimation 020201 artificial intelligence & image processing General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering Bernoulli randomness Electrical and Electronic Engineering lcsh:TK1-9971 Algorithm Randomness Sparse matrix |
| Zdroj: | IEEE Access, Vol 8, Pp 127818-127833 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3008534 |
| Popis: | MAP estimation plays an important role in many probabilistic models. However, in many cases, the MAP problem is non-convex and intractable. In this work, we propose a novel algorithm, called BOPE, which uses Bernoulli randomness for Online Maximum a Posteriori Estimation. We show that BOPE has a fast convergence rate. In particular, BOPE implicitly employs a prior which plays as regularization. Such a prior is different from the one of the MAP problem and will be vanishing as BOPE does more iterations. This property of BOPE is significant and enables to reduce severe overfitting for probabilistic models in ill-posed cases, including short text, sparse data, and noisy data. We validate the practical efficiency of BOPE in two contexts: text analysis and recommender systems. Both contexts show the superior of BOPE over the baselines. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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