Kronecker Determinantal Point Processes
Published in
Neural Information Processing Systems(2016)External Links:
TL
TL;DR
This work enables efficient sampling and learning for DPPs by introducing KronDPP, a DPP model whose kernel matrix decomposes as a tensor product of multiple smaller kernel matrices, which enables fast exact sampling.
Abstract
Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items. They have recently gained prominence in several applications that rely on "diverse" subsets. However, their applicability to large problems is still limited due to the $\mathcal O(N^3)$ complexity of core tasks such as sampling and learning. We enable efficient sampling and learning for DPPs by introducing KronDPP, a DPP model whose kernel matrix decomposes as a tensor product of multiple smaller kernel matrices. This decomposition immediately enables fast exact sampling. But contrary to what one may expect, leveraging the Kronecker product structure for speeding up DPP learning turns out to be more difficult. We overcome this challenge, and derive batch and stochastic optimization algorithms for efficiently learning the parameters of a KronDPP.
References28 items
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Determinantal Point Processes
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Fast DPP Sampling for Nystrom with Application to Kernel Methods
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Low-Rank Factorization of Determinantal Point Processes
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Fast Orthogonal Projection Based on Kronecker Product
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