A Generalization of Spatial Monte Carlo Integration
This work addresses a specific bottleneck in probabilistic graphical models for researchers in machine learning, offering incremental improvements to existing methods.
The paper tackles the limitation of Spatial Monte Carlo Integration (SMCI) in dense systems by proposing a generalization (GSMCI) with a proven accuracy bound, and introduces a new pairwise Boltzmann machine learning method combining SMCI with persistent contrastive divergence, which greatly improves learning accuracy.
Spatial Monte Carlo integration (SMCI) is an extension of standard Monte Carlo integration and can approximate expectations on Markov random fields with high accuracy. SMCI was applied to pairwise Boltzmann machine (PBM) learning, with superior results to those from some existing methods. The approximation level of SMCI can be changed, and it was proved that a higher-order approximation of SMCI is statistically more accurate than a lower-order approximation. However, SMCI as proposed in the previous studies suffers from a limitation that prevents the application of a higher-order method to dense systems. This study makes two different contributions as follows. A generalization of SMCI (called generalized SMCI (GSMCI)) is proposed, which allows relaxation of the above-mentioned limitation; moreover, a statistical accuracy bound of GSMCI is proved. This is the first contribution of this study. A new PBM learning method based on SMCI is proposed, which is obtained by combining SMCI and the persistent contrastive divergence. The proposed learning method greatly improves the accuracy of learning. This is the second contribution of this study.