Learning the Parameters of Determinantal Point Process Kernels
This addresses a known bottleneck in DPP parameter learning for applications requiring diversity, but it is incremental as it builds on existing Bayesian approaches.
The paper tackles the difficult problem of learning parameters for Determinantal Point Process kernels, which model repulsion for diversity, by proposing Bayesian methods that work in large-scale and continuous settings, demonstrating utility in medical and perception studies.
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing properties, such as efficient sampling, learning the parameters of a DPP is still considered a difficult problem due to the non-convex nature of the likelihood function. In this paper, we propose using Bayesian methods to learn the DPP kernel parameters. These methods are applicable in large-scale and continuous DPP settings even when the exact form of the eigendecomposition is unknown. We demonstrate the utility of our DPP learning methods in studying the progression of diabetic neuropathy based on spatial distribution of nerve fibers, and in studying human perception of diversity in images.