Foundations and Trends® in Machine Learning· 2012Q1· Review
Determinantal Point Processes for Machine Learning
- 626citations
- Q1SCImago
- 2012year
Short summary
Determinantal Point Processes (DPPs) offer efficient and exact algorithms for modeling repulsion, overcoming intractability issues found in traditional models with negative correlations.
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Key points
- DPPs are probabilistic models that inherently represent repulsion.
- They overcome the intractability and approximation challenges of traditional models (e.g., Markov random fields) when dealing with negative correlations.
- DPPs enable efficient and exact algorithms for key inference tasks like sampling and marginalization.
- Applications include selecting diverse sets for search results, document summarization, and pose modeling in images.
AI-generated from the title and abstract; the full text is not read.
Abstract
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. We provide a gentle introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and show how DPPs can be applied to real-world applications like finding diverse sets of high-quality search results, building informative summaries by selecting diverse sentences from documents, modeling nonoverlapping human poses in images or video, and automatically building timelines of important news stories.
The authors' abstract, as published at the source. Foundations and Trends® in Machine Learning, 2012 · DOI ↗
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Field: Applied Mathematics
Applied MathematicsMathematics