Diversified Bayesian nonnegative matrix factorization

Conference paper


Qiao, Maoying, Jun,Yu, Tongliang, Liu, Xinchao, Wang and Dacheng, Tao. (2020) Diversified Bayesian nonnegative matrix factorization. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). New York Hilton Midtown, New York, New York, United States of America 07 - 12 Feb 2020 AAAI Press. pp. 5420-5427 https://doi.org/10.1609/aaai.v34i04.5991
AuthorsQiao, Maoying, Jun,Yu, Tongliang, Liu, Xinchao, Wang and Dacheng, Tao
TypeConference paper
Abstract

Nonnegative matrix factorization (NMF) has been widely employed in a variety of scenarios due to its capability of inducing semantic part-based representation. However, because of the non-convexity of its objective, the factorization is generally not unique and may inaccurately discover intrinsic “parts” from the data. In this paper, we approach this issue using a Bayesian framework. We propose to assign a diversity prior to the parts of the factorization to induce correctness based on the assumption that useful parts should be distinct and thus well-spread. A Bayesian framework including this diversity prior is then established. This framework aims at inducing factorizations embracing both good data fitness from maximizing likelihood and large separability from the diversity prior. Specifically, the diversity prior is formulated with determinantal point processes (DPP) and is seamlessly embedded into a Bayesian NMF framework. To carry out the inference, a Monte Carlo Markov Chain (MCMC) based procedure is derived. Experiments conducted on a synthetic dataset and a real-world MULAN dataset for multi-label learning (MLL) task demonstrate the superiority of the proposed method.

Year2020
JournalProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN2374-3468
2159-5399
Digital Object Identifier (DOI)https://doi.org/10.1609/aaai.v34i04.5991
Publisher's version
License
All rights reserved
File Access Level
Controlled
Journal citation34 (4), pp. 5420-5427
Page range5420-5427
ISBN9781577358664
FunderAustralian Research Council
Output statusPublished
Publication dates
Online03 Apr 2020
Publication process dates
Deposited22 Jun 2021
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDARC/FL170100117
ARC/DP180103424
ARC/IH180100002
ARC/DE190101473
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