ArticleIEEE access : practical innovations, open solutions2018
Multi-View Maximum Entropy Clustering by Jointly Leveraging Inter-View Collaborations and Intra-View-Weighted Attributes.
Article in IEEE access : practical innovations, open solutions, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation.ACM transactions on intelligent systems and technology · 2024Article
- Segmentation of Brain Tissues from MRI Images Using Multitask Fuzzy Clustering Algorithm.Journal of healthcare engineering · 2023Article
- QOGMP: QoS-oriented global multi-path traffic scheduling algorithm in software defined network.Scientific reports · 2022Article
- Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm.Frontiers in neuroscience · 2021Article
- A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View FuzzyFrontiers in neuroscience · 2021Article
- Home Textile Pattern Emotion Labeling Using Deep Multi-View Feature Learning.Frontiers in psychology · 2021Article
- mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.IEEE transactions on medical imaging · 2020Article
- An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.Computational and mathematical methods in medicine · 2020Article
- Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network.Journal of medical systems · 2019Article
- Multiscale Time-Sharing Elastography Algorithms and Transfer Learning of Clinicopathological Features of Uterine Cervical Cancer for Medical Intelligent Computing System.Journal of medical systems · 2019Article
- An Improved Full Convolutional Network Combined with Conditional Random Fields for Brain MR Image Segmentation Algorithm and its 3D Visualization Analysis.Journal of medical systems · 2019Article
- Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model.Journal of medical systems · 2019Article
- Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature.Journal of medical systems · 2019Article
- A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation.Journal of medical systems · 2019Article
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Authors and funding
8 authors.
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Abstract
As a dedicated countermeasure for heterogeneous multi-view data, multi-view clustering is currently a hot topic in machine learning. However, many existing methods either neglect the effective collaborations among views during clustering or do not distinguish the respective importance of attributes in views, instead treating them equivalently. Motivated by such challenges, based on maximum entropy clustering (MEC), two specialized criteria-inter-view collaborative learning (IEVCL) and intra-view-weighted attributes (IAVWA)-are first devised as the bases. Then, by organically incorporating IEVCL and IAVWA into the formulation of classic MEC, a novel, collaborative multi-view clustering model and the matching algorithm referred to as the view-collaborative, attribute-weighted MEC (VC-AW-MEC) are proposed. The significance of our efforts is three-fold: 1) both IEVCL and IAVWA are dedicatedly devised based on MEC so that the proposed VC-AW-MEC is qualified to effectively handle as many multi-view data scenes as possible; 2) IEVCL is competent in seeking the consensus across all involved views throughout clustering, whereas IAVWA is capable of adaptively discriminating the individual impact regarding the attributes within each view; and 3) benefiting from jointly leveraging IEVCL and IAVWA, compared with some existing state-of-the-art approaches, the proposed VC-AW-MEC algorithm generally exhibits preferable clustering effectiveness and stability on heterogeneous multi-view data. Our efforts have been verified in many synthetic or real-world multi-view data scenes.
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