Results 251 to 260 of about 134,390 (294)

Partial Multi-View Clustering [PDF]

Proceedings of the AAAI Conference on Artificial Intelligence, 2014
Real data are often with multiple modalities or comingfrom multiple channels, while multi-view clusteringprovides a natural formulation for generating clustersfrom such data. Previous studies assumed that each exampleappears in all views, or at least there is one viewcontaining all examples.
Shao-Yuan Li, Yuan Jiang 0001, Zhi-Hua Zhou   +2 more
openaire   +2 more sources

Binary Multi-View Clustering [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
Clustering is a long-standing important research problem, however, remains challenging when handling large-scale image data from diverse sources. In this paper, we present a novel Binary Multi-View Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data.
Zheng Zhang 0006, Li Liu 0004, Fumin Shen, Heng Tao Shen, Ling Shao 0001   +4 more
openaire   +6 more sources

Consensus Guided Multi-View Clustering [PDF]

ACM Transactions on Knowledge Discovery from Data, 2018
In recent decades, tremendous emerging techniques thrive the artificial intelligence field due to the increasing collected data captured from multiple sensors. These multi-view data provide more rich information than traditional single-view data.
Hongfu Liu 0001, Yun Fu 0001
openaire   +2 more sources

A Survey and an Empirical Evaluation of Multi-View Clustering Approaches [PDF]

ACM Computing Surveys
Supplementary Material is available online at: https://dl.acm.org/doi/10.1145/3645108#supplementary-materials .Code is availailable online at: https://github.com/dugzzuli/A-Survey-of-Multi-view-Clustering-Approaches .Multi-view clustering (MVC) holds a ...
Lihua Zhou, Guowang Du, Kevin Lu
exaly   +3 more sources

An overview of recent multi-view clustering [PDF]

Neurocomputing, 2020
© 2020 Elsevier B.V. With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available.
Lele Fu, Athanasios V Vasilakos, Shiping Wang   +2 more
exaly   +2 more sources

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Multi-View Clustering

Fourth IEEE International Conference on Data Mining (ICDM'04), 2005
We consider clustering problems in which the available attributes can be split into two independent subsets, such that either subset suffices for learning. Example applications of this multi-view setting include clustering of Web pages which have an intrinsic view (the pages themselves) and an extrinsic view (e.g., anchor texts of inbound hyperlinks ...
Steffen Bickel, Tobias Scheffer
openaire   +1 more source

Dependence-Guided Multi-View Clustering

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
In this paper, we propose a novel approach called dependence-guided multi-view clustering (DGMC). Our model enhances the dependence between unified embedding learning and clustering, as well as promotes the dependence between unified embedding and embedding of each view.
Xia Dong, Danyang Wu, Feiping Nie 0001, Rong Wang 0001, Xuelong Li 0001   +4 more
openaire   +1 more source

Collaborative multi-view clustering

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
The purpose of this article is to introduce a new collaborative multi-view clustering approach based on a probabilistic model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques.
Mohamad Ghassany, Nistor Grozavu, Younès Bennani   +2 more
openaire   +1 more source

Multi-view clustering ensembles

2013 International Conference on Machine Learning and Cybernetics, 2013
Multi-view clustering and clustering ensembles have become increasingly popular in recent years. Multi-view clustering employs the relationship between views to cluster data; clustering ensembles combine different component clusterings to a better final partition.
Xijiong Xie, Shiliang Sun
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Multi-view clustering with interactive mechanism

Neurocomputing, 2021
Abstract Existing multi-view clustering methods either seek to directly learn a consistent spectral embedding, or to learn a consistent graph. This work presents a novel model, called Multi-view Clustering with Interactive Mechanism (MCIM). Using the interactive mechanism, the uniform graph and spectral embedding can be learned alternatively and ...
Danyang Wu, Zhanxuan Hu, Feiping Nie 0001, Rong Wang 0001, Hui Yang 0005, Xuelong Li 0001   +5 more
openaire   +1 more source