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short-paper

Relational co-clustering via manifold ensemble learning

Authors:

Zhanying HeAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 1687 - 1691

https://doi.org/10.1145/2396761.2398498

Published: 29 October 2012 Publication History

Abstract

Co-clustering targets on grouping the samples and features simultaneously. It takes advantage of the duality between the samples and features. In many real-world applications, the data points or features usually reside on a submanifold of the ambient Euclidean space, but it is nontrivial to estimate the intrinsic manifolds in a principled way. In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which aims to maximally approximate the intrinsic manifolds of both the sample and feature spaces. To achieve this, we develop a novel co-clustering algorithm called Relational Multi-manifold Co-clustering (RMC) based on symmetric nonnegative matrix tri-factorization, which decomposes the relational data matrix into three matrices. This method considers the inter-type relationship revealed by the relational data matrix and the intra-type information reflected by the affinity matrices. Specifically, we assume the intrinsic manifold of the sample or feature space lies in a convex hull of a group of pre-defined candidate manifolds. We hope to learn an appropriate convex combination of them to approach the desired intrinsic manifold. To optimize the objective, the multiplicative rules are utilized to update the factorized matrices and the entropic mirror descent algorithm is exploited to automatically learn the manifold coefficients. Experimental results demonstrate the superiority of the proposed algorithm.

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Cited By

Wang HSong YChen WLuo ZLi CLi T(2024)A Survey of Co-ClusteringACM Transactions on Knowledge Discovery from Data10.1145/368179318:9(1-28)Online publication date: 25-Jul-2024
https://dl.acm.org/doi/10.1145/3681793
Nayak RLuong KNayak RLuong K(2023)NMF and Manifold Learning for Multi-aspect DataMulti-aspect Learning10.1007/978-3-031-33560-0_3(51-76)Online publication date: 28-Jul-2023
https://doi.org/10.1007/978-3-031-33560-0_3
Nayak RLuong KNayak RLuong K(2023)Non-negative Matrix Factorization-Based Multi-aspect Data ClusteringMulti-aspect Learning10.1007/978-3-031-33560-0_2(27-50)Online publication date: 28-Jul-2023
https://doi.org/10.1007/978-3-031-33560-0_2
Show More Cited By

Index Terms

Relational co-clustering via manifold ensemble learning
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

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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Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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

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Cited By

Wang HSong YChen WLuo ZLi CLi T(2024)A Survey of Co-ClusteringACM Transactions on Knowledge Discovery from Data10.1145/368179318:9(1-28)Online publication date: 25-Jul-2024
https://dl.acm.org/doi/10.1145/3681793
Nayak RLuong KNayak RLuong K(2023)NMF and Manifold Learning for Multi-aspect DataMulti-aspect Learning10.1007/978-3-031-33560-0_3(51-76)Online publication date: 28-Jul-2023
https://doi.org/10.1007/978-3-031-33560-0_3
Nayak RLuong KNayak RLuong K(2023)Non-negative Matrix Factorization-Based Multi-aspect Data ClusteringMulti-aspect Learning10.1007/978-3-031-33560-0_2(27-50)Online publication date: 28-Jul-2023
https://doi.org/10.1007/978-3-031-33560-0_2
Wang DLi TDeng PWang HZhang P(2022)Dual graph-regularized sparse concept factorization for clusteringInformation Sciences: an International Journal10.1016/j.ins.2022.05.101607:C(1074-1088)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.05.101
Luong KNayak R(2020)A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00080(865-876)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00080
Gayani Tennakoon TLuong KMohotti WChakravarthy SNayak R(2019)Multi-type Relational Data Clustering for Community Detection by Exploiting Content and Structure Information in Social NetworksPRICAI 2019: Trends in Artificial Intelligence10.1007/978-3-030-29911-8_42(541-554)Online publication date: 23-Aug-2019
https://doi.org/10.1007/978-3-030-29911-8_42
Luong KNayak R(2018)Learning Association Relationship and Accurate Geometric Structures for Multi-Type Relational Data2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00053(509-520)Online publication date: Apr-2018
https://doi.org/10.1109/ICDE.2018.00053
Luong KBalasubramaniam TNayak R(2018)A Novel Technique of Using Coupled Matrix and Greedy Coordinate Descent for Multi-view Data RepresentationWeb Information Systems Engineering – WISE 201810.1007/978-3-030-02925-8_20(285-300)Online publication date: 21-Oct-2018
https://doi.org/10.1007/978-3-030-02925-8_20
Luong KNayak R(2018)Clustering Multi-View Data Using Non-negative Matrix Factorization and Manifold Learning for Effective Understanding: A Survey PaperLinking and Mining Heterogeneous and Multi-view Data10.1007/978-3-030-01872-6_9(201-227)Online publication date: 27-Nov-2018
https://doi.org/10.1007/978-3-030-01872-6_9
Zhang Y(2017)A Kernel Probabilistic Model for Semi-supervised Co-clustering EnsembleJournal of Intelligent Systems10.1515/jisys-2017-051329:1(143-153)Online publication date: 30-Dec-2017
https://doi.org/10.1515/jisys-2017-0513
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