Learning Determinantal Point Processes in Sublinear Time

Christophe Dupuy; Francis Bach

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arXiv · 1610.05925

Learning Determinantal Point Processes in Sublinear Time

Christophe Dupuy·Francis Bach

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Abstract

We propose a new class of determinantal point processes (DPPs) which can be manipulated for inference and parameter learning in potentially sublinear time in the number of items. This class, based on a specific low-rank factorization of the marginal kernel, is particularly suited to a subclass of continuous DPPs and DPPs defined on exponentially many items. We apply this new class to modelling text documents as sampling a DPP of sentences, and propose a conditional maximum likelihood formulation to model topic proportions, which is made possible with no approximation for our class of DPPs. We present an application to document summarization with a DPP on $2^{500}$ items.

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BibTeX RIS

Christophe Dupuy, Francis Bach. 2016-10-19. Learning Determinantal Point Processes in Sublinear Time. https://arxiv.org/abs/1610.05925

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