Fast sampling for time-varying determinantal point processes

Journal article


Qiao, Maoying, Xu, Richard Yi Da, Bian, Wei and Tao, Dacheng. (2016). Fast sampling for time-varying determinantal point processes. ACM Transactions on Knowledge Discovery from Data. 11(1), p. 8. https://doi.org/1556-4681
AuthorsQiao, Maoying, Xu, Richard Yi Da, Bian, Wei and Tao, Dacheng
Abstract

Determinantal Point Processes (DPPs) are stochastic models which assign each subset of a base dataset with a probability proportional to the subset’s degree of diversity. It has been shown that DPPs are particularly appropriate in data subset selection and summarization (e.g., news display, video summarizations). DPPs prefer diverse subsets while other conventional models cannot offer. However, DPPs inference algorithms have a polynomial time complexity which makes it difficult to handle large and time-varying datasets, especially when real-time processing is required. To address this limitation, we developed a fast sampling algorithm for DPPs which takes advantage of the nature of some time-varying data (e.g., news corpora updating, communication network evolving), where the data changes between time stamps are relatively small. The proposed algorithm is built upon the simplification of marginal density functions over successive time stamps and the sequential Monte Carlo (SMC) sampling technique. Evaluations on both a real-world news dataset and the Enron Corpus confirm the efficiency of the proposed algorithm.

Keywordsinformation systems; information systems applications; data mining; spatialtemporal systems; time-varying determinantal point processes (tv-dpps); sequential; Monte Carlo; fast sampling
Year2016
JournalACM Transactions on Knowledge Discovery from Data
Journal citation11 (1), p. 8
PublisherAssociation for Computing Machinery
ISSN1556-4681
Digital Object Identifier (DOI)https://doi.org/1556-4681
Scopus EID2-s2.0-84979882467
Research or scholarlyResearch
Page range1-24
FunderAustralian Research Council
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online20 Jul 2016
Publication process dates
AcceptedMay 2016
Deposited14 Jun 2021
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDARC/FT130101457
ARC/DP140102164
ARC/LE140100061
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