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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Xuan Bui
Thuyloi University
Computer Science & IT Engineering

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GS-OPT: A new fast stochastic algorithm for solving the non-convex optimization problem Xuan Bui; Nhung Duong; Trung Hoang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (730.381 KB) | DOI: 10.11591/ijai.v9.i2.pp183-192

Abstract

Non-convex optimization has an important role in machine learning. However, the theoretical understanding of non-convex optimization remained rather limited. Studying efficient algorithms for non-convex optimization has attracted a great deal of attention from many researchers around the world but these problems are usually NP-hard to solve. In this paper, we have proposed a new algorithm namely GS-OPT (General Stochastic OPTimization) which is effective for solving the non-convex problems. Our idea is to combine two stochastic bounds of the objective function where they are made by a commonly discrete probability distribution namely Bernoulli. We consider GS-OPT carefully on both the theoretical and experimental aspects. We also apply GS-OPT for solving the posterior inference problem in the latent Dirichlet allocation. Empirical results show that our approach is often more efficient than previous ones.
A stochastic algorithm for solving the posterior inference problem in topic models Hoang Quang Trung; Xuan Bui
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.23764

Abstract

Latent Dirichlet allocation (LDA) is an important probabilistic generative model and has usually used in many domains such as text mining, retrieving information, or natural language processing domains. The posterior inference is the important problem in deciding the quality of the LDA model, but it is usually non-deterministic polynomial (NP)-hard and often intractable, especially in the worst case. For individual texts, some proposed methods such as variational Bayesian (VB), collapsed variational Bayesian (CVB), collapsed Gibb’s sampling (CGS), and online maximum a posteriori estimation (OPE) to avoid solving this problem directly, but they usually do not have any guarantee of convergence rate or quality of learned models excepting variants of OPE. Based on OPE and using the Bernoulli distribution combined, we design an algorithm namely general online maximum a posteriori estimation using two stochastic bounds (GOPE2) for solving the posterior inference problem in LDA model. It also is the NP-hard non-convex optimization problem. Via proof of theory and experimental results on the large datasets, we realize that GOPE2 is performed to develop the efficient method for learning topic models from big text collections especially massive/streaming texts, and more efficient than previous methods.
Co-Authors Hoang Quang Trung Nhung Duong Trung Hoang