Learning a Bayesian network with multiple latent variables for implicit relation representation

• Published in: Data mining and knowledge discovery Vol. 38; no. 4; pp. 1634 - 1669
• Main Authors: Wu, Xinran, Yue, Kun, Duan, Liang, Fu, Xiaodong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bayesian analysis; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Design optimization; Efficiency; Etiology; Graph theory; Information Storage and Retrieval; Machine learning; Optimization; Physics; Preferences; Probabilistic inference; Random variables; Statistics for Engineering; Latent variable; Incremental learning; Parallel algorithm; Implicit relation; Bayesian network
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-024-01012-3

Artificial intelligence applications could be more powerful and comprehensive by incorporating the ability of inference, which could be achieved by probabilistic inference over implicit relations. It is significant yet challenging to represent implicit relations among observed variables and latent ones like disease etiologies and user preferences. In this paper, we propose the BN with multiple latent variables (MLBN) as the framework for representing the dependence relations, where multiple latent variables are incorporated to describe multi-dimensional abstract concepts. However, the efficiency of MLBN learning and effectiveness of MLBN based applications are still nontrivial due to the presence of multiple latent variables. To this end, we first propose the constraint induced and Spark based algorithm for MLBN learning, as well as several optimization strategies. Moreover, we present the concept of variation degree and further design a subgraph based algorithm for incremental learning of MLBN. Experimental results suggest that our proposed MLBN model could represent the dependence relations correctly. Our proposed method outperforms some state-of-the-art competitors for personalized recommendation, and facilitates some typical approaches to achieve better performance.