Stochastic Patching Process
This addresses a limitation in partition models for multi-dimensional data, offering a more parsimonious approach, though it appears incremental as it builds on existing stochastic partition models.
The paper tackles the problem of unnecessary dissections in sparse regions when partitioning multi-dimensional arrays by proposing the Stochastic Patching Process (SPP), which uses an enclosing strategy to attach patches to dense regions, and experimental results show it outperforms state-of-the-art methods.
Stochastic partition models tailor a product space into a number of rectangular regions such that the data within each region exhibit certain types of homogeneity. Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions. To alleviate this limitation, we propose a parsimonious partition model, named Stochastic Patching Process (SPP), to deal with multi-dimensional arrays. SPP adopts an "enclosing" strategy to attach rectangular patches to dense regions. SPP is self-consistent such that it can be extended to infinite arrays. We apply SPP to relational modeling and the experimental results validate its merit compared to the state-of-the-arts.