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Robust Non-negative Matrix Factorization Based on Noise Fuzzy Clustering Mechanism

Authors:

Masaaki Ueno,

Katsuhiro Honda,

Seiki Ubukata,

Akira NotsuAuthors Info & Claims

AICCC '19: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference

Pages 1 - 5

https://doi.org/10.1145/3375959.3375966

Published: 16 February 2020 Publication History

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Abstract

Nonnegative matrix factorization (NMF) is a basic decomposition method of matrices consisting only of nonnegative values and has been utilized in various fields including air pollution analysis. This paper proposes an approach for noise rejection in NMF through noise clustering mechanism with the goal of eliminating the influence of noise observation. Robust estimation is realized by utilizing the least squares criterion of NMF not only for NMF model estimation but also for calculating the degree of belongingness to noise clusters in the framework of alternate optimization. The characteristics of the proposed method is demonstrated in a toy example with an artificial data set followed by a task of air pollutant measurement analysis.

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Cited By

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Okabe AHonda KUbukata S(2022)Noise Fuzzy Clustering-Based Robust Non-negative Matrix Factorization with I-divergence CriterionIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-030-98018-4_21(256-266)Online publication date: 18-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-030-98018-4_21
Honda KHoshii KUbukata SNotsu A(2021)Robust fuzzy factorization machine with noise clustering-based membership function estimationSoft Computing Letters10.1016/j.socl.2021.1000243(100024)Online publication date: Dec-2021
https://doi.org/10.1016/j.socl.2021.100024

Index Terms

Robust Non-negative Matrix Factorization Based on Noise Fuzzy Clustering Mechanism
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Non-negative matrix factorization
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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Published In

AICCC '19: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference

December 2019

216 pages

ISBN:9781450372633

DOI:10.1145/3375959

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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Kobe University: Kobe University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 February 2020

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AICCC 2019

AICCC 2019: 2019 2nd Artificial Intelligence and Cloud Computing Conference

December 21 - 23, 2019

Kobe, Japan

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Cited By

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Okabe AHonda KUbukata S(2022)Noise Fuzzy Clustering-Based Robust Non-negative Matrix Factorization with I-divergence CriterionIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-030-98018-4_21(256-266)Online publication date: 18-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-030-98018-4_21
Honda KHoshii KUbukata SNotsu A(2021)Robust fuzzy factorization machine with noise clustering-based membership function estimationSoft Computing Letters10.1016/j.socl.2021.1000243(100024)Online publication date: Dec-2021
https://doi.org/10.1016/j.socl.2021.100024

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Recommendations

Heuristics for exact nonnegative matrix factorization

Quadratic nonnegative matrix factorization

Weighted Nonnegative Matrix Tri-Factorization for Co-clustering