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Two-view feature generation model for semi-supervised learning

Authors:

Rie Kubota Ando,

Tong ZhangAuthors Info & Claims

ICML '07: Proceedings of the 24th international conference on Machine learning

Pages 25 - 32

https://doi.org/10.1145/1273496.1273500

Published: 20 June 2007 Publication History

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Abstract

We consider a setting for discriminative semi-supervised learning where unlabeled data are used with a generative model to learn effective feature representations for discriminative training. Within this framework, we revisit the two-view feature generation model of co-training and prove that the optimum predictor can be expressed as a linear combination of a few features constructed from unlabeled data. From this analysis, we derive methods that employ two views but are very different from co-training. Experiments show that our approach is more robust than co-training and EM, under various data generation conditions.

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Cited By

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Zhang MPeng MZhou Y(2025)Leveraging Unlabeled Data for Superior ROC Curve Estimation via a Semiparametric ApproachJournal of Business & Economic Statistics10.1080/07350015.2025.2450495(1-32)Online publication date: 7-Jan-2025
https://doi.org/10.1080/07350015.2025.2450495
Agraz MDeng YKarniadakis GMantzoros C(2024)Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced datasetScientific Reports10.1038/s41598-024-69844-z14:1Online publication date: 30-Sep-2024
https://doi.org/10.1038/s41598-024-69844-z
Jiao XPeng MZhou Y(2024)Smoothed Estimation on Optimal Treatment Regime Under Semisupervised Setting in Randomized TrialsBiometrical Journal10.1002/bimj.7000666:8Online publication date: 23-Nov-2024
https://doi.org/10.1002/bimj.70006
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Two-view feature generation model for semi-supervised learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

ICML '07: Proceedings of the 24th international conference on Machine learning

June 2007

1233 pages

ISBN:9781595937933

DOI:10.1145/1273496

Editor:
Zoubin Ghahramani
University of Cambridge, United Kingdom

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2007

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ICML '07 & ILP '07

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ICML '07 & ILP '07: The 24th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 20 - 24, 2007

Oregon, Corvalis, USA

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Zhang MPeng MZhou Y(2025)Leveraging Unlabeled Data for Superior ROC Curve Estimation via a Semiparametric ApproachJournal of Business & Economic Statistics10.1080/07350015.2025.2450495(1-32)Online publication date: 7-Jan-2025
https://doi.org/10.1080/07350015.2025.2450495
Agraz MDeng YKarniadakis GMantzoros C(2024)Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced datasetScientific Reports10.1038/s41598-024-69844-z14:1Online publication date: 30-Sep-2024
https://doi.org/10.1038/s41598-024-69844-z
Jiao XPeng MZhou Y(2024)Smoothed Estimation on Optimal Treatment Regime Under Semisupervised Setting in Randomized TrialsBiometrical Journal10.1002/bimj.7000666:8Online publication date: 23-Nov-2024
https://doi.org/10.1002/bimj.70006
Song SLin YZhou Y(2023)A General M-estimation Theory in Semi-Supervised FrameworkJournal of the American Statistical Association10.1080/01621459.2023.2169699119:546(1065-1075)Online publication date: 28-Feb-2023
https://doi.org/10.1080/01621459.2023.2169699
Wang TTang WLin YSu W(2023)Semi‐supervised inference for nonparametric logistic regressionStatistics in Medicine10.1002/sim.973742:15(2573-2589)Online publication date: 10-May-2023
https://doi.org/10.1002/sim.9737
Lee JLei QSaunshi NZhuo JRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Predicting what you already know helpsProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540285(309-323)Online publication date: 6-Dec-2021
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Kumar AKumar VKumari S(2021)A Graph Coloring Based Framework for Views Construction in Multi-View Ensemble Learning2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC)10.1109/ICSCCC51823.2021.9478138(84-89)Online publication date: 21-May-2021
https://doi.org/10.1109/ICSCCC51823.2021.9478138
Huang CPan LYang QWang HShao J(2021)Flexible, Robust, Scalable Semi-supervised Learning via Reliability Propagation2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00030(200-209)Online publication date: Dec-2021
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Alam MKumar VKumar A(2021)A Multi-view Convolutional Neural Network Approach for Image Data Classification2021 International Conference on Communication information and Computing Technology (ICCICT)10.1109/ICCICT50803.2021.9509943(1-6)Online publication date: 25-Jun-2021
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Feng ZHuang GChi D(2020)Classification of the Complex Agricultural Planting Structure with a Semi-Supervised Extreme Learning Machine FrameworkRemote Sensing10.3390/rs1222370812:22(3708)Online publication date: 12-Nov-2020
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Cross-Domain Semi-Supervised Learning Using Feature Formulation

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