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Probabilistic Non-negative Inconsistent-resolution Matrices Factorization

Authors:

Masahiro Kohjima,

Tatsushi Matsubayashi,

Hiroshi SawadaAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1855 - 1858

https://doi.org/10.1145/2806416.2806636

Published: 17 October 2015 Publication History

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Abstract

In this paper, we tackle with the problem of analyzing datasets with different resolution such as a pair of user's individual data and user group's data, for example "userA visited shopA 5 times" and "users whose attributes are men purchased itemA 80 times in total". In order to establish a basic approach to this problem, we focus on the simplified scenario and propose a new probabilistic model called probabilistic non-negative inconsistent-resolution matrices factorization (pNimf). pNimf is rigorously derived from the data generative process using latent high-resolution data which underlie low-resolution data. We conduct experiments on real purchase log data and confirm that the proposed model provides superior performance, and that the performance improves as the number of low-resolution data increases. These results imply that our way of modeling using latent high-resolution data can become the basic approach to the problem of analyzing dataset with different resolution.

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Cited By

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Kohjima M(2024)On The Equivalence Of Dynamic Mode Decomposition And Complex Nonnegative Matrix FactorizationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448067(5800-5804)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448067
Hayashi N(2020)Variational approximation error in non-negative matrix factorizationNeural Networks10.1016/j.neunet.2020.03.009126(65-75)Online publication date: Jun-2020
https://doi.org/10.1016/j.neunet.2020.03.009
Hayashi NWatanabe S(2020)Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix FactorizationSN Computer Science10.1007/s42979-020-0071-31:2Online publication date: 20-Feb-2020
https://doi.org/10.1007/s42979-020-0071-3
Show More Cited By

Index Terms

Probabilistic Non-negative Inconsistent-resolution Matrices Factorization
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Information

Published In

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2015

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Kohjima M(2024)On The Equivalence Of Dynamic Mode Decomposition And Complex Nonnegative Matrix FactorizationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448067(5800-5804)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448067
Hayashi N(2020)Variational approximation error in non-negative matrix factorizationNeural Networks10.1016/j.neunet.2020.03.009126(65-75)Online publication date: Jun-2020
https://doi.org/10.1016/j.neunet.2020.03.009
Hayashi NWatanabe S(2020)Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix FactorizationSN Computer Science10.1007/s42979-020-0071-31:2Online publication date: 20-Feb-2020
https://doi.org/10.1007/s42979-020-0071-3
KOHJIMA MMATSUBAYASHI TSAWADA H(2019)Learning of Nonnegative Matrix Factorization Models for Inconsistent Resolution Dataset AnalysisIEICE Transactions on Information and Systems10.1587/transinf.2018AWI0002E102.D:4(715-723)Online publication date: 1-Apr-2019
https://doi.org/10.1587/transinf.2018AWI0002
Kohjima MMatsubayashi TToda H(2019)Generalized Interval Valued Nonnegative Matrix FactorizationICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2019.8682181(3412-3416)Online publication date: May-2019
https://doi.org/10.1109/ICASSP.2019.8682181
Hayashi NWatanabe S(2017)Tighter upper bound of real log canonical threshold of non-negative matrix factorization and its application to Bayesian inference2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8280811(1-8)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8280811
Kohjima MWatanabe S(2017)Phase Transition Structure of Variational Bayesian Nonnegative Matrix FactorizationArtificial Neural Networks and Machine Learning – ICANN 201710.1007/978-3-319-68612-7_17(146-154)Online publication date: 25-Oct-2017
https://doi.org/10.1007/978-3-319-68612-7_17

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