Article

Convex Mixture Models for Multi-view Clustering

Authors:

Grigorios Tzortzis,

Aristidis LikasAuthors Info & Claims

ICANN '09: Proceedings of the 19th International Conference on Artificial Neural Networks: Part II

Pages 205 - 214

https://doi.org/10.1007/978-3-642-04277-5_21

Published: 02 October 2009 Publication History

Abstract

Data with multiple representations (views) arise naturally in many applications and multi-view algorithms can substantially improve the classification and clustering results. In this work, we study the problem of multi-view clustering and propose a multi-view convex mixture model that locates exemplars (cluster representatives) in the dataset by simultaneously considering all views. Convex mixture models are simplified mixture models that exhibit several attractive characteristics. The proposed algorithm extends the single view convex mixture models so as to handle data with any number of representations, taking into account the diversity of the views while preserving their good properties. Empirical evaluations on synthetic and real data demonstrate the effectiveness and potential of our method.

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Cited By

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Haris MYusoff YMohd Zain AKhattak AHussain S(2024)Breaking down multi-view clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107857132:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.107857
Mitra SHasanuzzaman MSaha S(2020)A Unified Multi-view Clustering Algorithm Using Multi-objective Optimization Coupled with Generative ModelACM Transactions on Knowledge Discovery from Data10.1145/336567314:1(1-31)Online publication date: 3-Feb-2020
https://dl.acm.org/doi/10.1145/3365673
Bettoumi SJlassi CArous N(2019)Collaborative multi-view K-means clusteringSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2801-623:3(937-945)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00500-017-2801-6
Show More Cited By

Convex Mixture Models for Multi-view Clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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Published In

ICANN '09: Proceedings of the 19th International Conference on Artificial Neural Networks: Part II

October 2009

996 pages

ISBN:9783642042768

Editors:
Cesare Alippi
Dipartimento di Elettronica, Politecnico di Milano, Milano, Italy 20133
,
Marios Polycarpou
Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus 1678
,
Christos Panayiotou
Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus 1678
,
Georgios Ellinas
Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus 1678

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Springer-Verlag

Berlin, Heidelberg

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Published: 02 October 2009

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Haris MYusoff YMohd Zain AKhattak AHussain S(2024)Breaking down multi-view clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107857132:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.107857
Mitra SHasanuzzaman MSaha S(2020)A Unified Multi-view Clustering Algorithm Using Multi-objective Optimization Coupled with Generative ModelACM Transactions on Knowledge Discovery from Data10.1145/336567314:1(1-31)Online publication date: 3-Feb-2020
https://dl.acm.org/doi/10.1145/3365673
Bettoumi SJlassi CArous N(2019)Collaborative multi-view K-means clusteringSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2801-623:3(937-945)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00500-017-2801-6
Valmarska AMiljkovic DLavraă? NRobnik-?Ikonja M(2018)Analysis of medications change in Parkinson's disease progression dataJournal of Intelligent Information Systems10.1007/s10844-018-0502-y51:2(301-337)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10844-018-0502-y
Tzortzis GLikas A(2010)Multiple view clustering using a weighted combination of exemplar-based mixture modelsIEEE Transactions on Neural Networks10.1109/TNN.2010.208199921:12(1925-1938)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1109/TNN.2010.2081999

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Mixture model averaging for clustering

Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

Mixture correntropy based robust multi-view K-means clustering

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