MAP Estimation With Bernoulli Randomness, and Its Application to Text Analysis and Recommender Systems

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 fas...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.127818-127833
Main Authors: Bui, Xuan, Vu, Hieu, Nguyen, Oanh, Than, Khoat
Format: Article
Language:English
Subjects:
Algorithms
Bernoulli randomness
Convergence
Estimation
Linear programming
online MAP estimation
Optimization
Probabilistic logic
Probabilistic models
Probability theory
Randomness
Recommender systems
Regularization
Systems analysis
Text analysis
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3008534