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Learning the Parameters of Determinantal Point Process Kernels
Authors: Raja H. Affandi, Emily Fox, Ryan Adams and Ben Taskar
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in applications where diversity is desired. While DPPs have many appealing properties, learning the parameters of a DPP is diffcult, as the likelihood is non-convex and is infeasible to compute in many scenarios. Here we propose Bayesian methods for learning the DPP kernel parameters. These methods are applicable in large-scale discrete and continuous DPP settings, even when the likelihood can only be bounded. We demonstrate the utility of our DPP learning methods in studying the progression of diabetic neuropathy based on the spatial distribution of nerve fibers, and in studying human perception of diversity in images.
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