Learning with Labeled and Unlabeled Multi-Step Transition Data for Recovering Markov Chain from Incomplete Transition Data
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 2412-2419. https://doi.org/10.24963/ijcai.2020/334
Due to the difficulty of comprehensive data collection, created by factors such as privacy protection and sensor device limitations, we often need to analyze incomplete transition data where some information is missing from the ideal (complete) transition data. In this paper, we propose a new method that can estimate, in a unified manner, Markov chain parameters from incomplete transition data that consist of hidden transition data (data from which visited state information is partially hidden) and dropped transition data (data from which some state visits are dropped). A key to developing the method is regarding the hidden and dropped transition data as labeled and unlabeled multi-step transition data, where the labels represent the number of steps required for each transition. This allows us to describe the generative process of multi-step transition data, and thus develop a new probabilistic model. We confirm the effectiveness of the proposal by experiments on synthetic and real data.
Machine Learning: Learning Generative Models
Machine Learning: Semi-Supervised Learning
Machine Learning: Unsupervised Learning