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Semi-supervised manifold ordinal regression for image ranking

Published: 28 November 2011 Publication History

Abstract

In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.

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Cited By

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  • (2024)Tackle balancing constraints in semi-supervised ordinal regressionMachine Learning10.1007/s10994-024-06518-x113:5(2575-2595)Online publication date: 4-Mar-2024
  • (2023)Multi-task Ordinal Regression with Labeled and Unlabeled DataInformation Sciences10.1016/j.ins.2023.119669(119669)Online publication date: Sep-2023
  • (2021)Semisupervised Ordinal Regression Based on Empirical Risk MinimizationNeural Computation10.1162/neco_a_0144533:12(3361-3412)Online publication date: 12-Nov-2021
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    cover image ACM Conferences
    MM '11: Proceedings of the 19th ACM international conference on Multimedia
    November 2011
    944 pages
    ISBN:9781450306164
    DOI:10.1145/2072298
    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: 28 November 2011

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    Author Tags

    1. image ranking
    2. manifold learning
    3. manifold ordinal regression
    4. ordinal regression
    5. semi-supervised learning
    6. semi-supervised manifold ordinal regression

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    MM '11: ACM Multimedia Conference
    November 28 - December 1, 2011
    Arizona, Scottsdale, USA

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

    View all
    • (2024)Tackle balancing constraints in semi-supervised ordinal regressionMachine Learning10.1007/s10994-024-06518-x113:5(2575-2595)Online publication date: 4-Mar-2024
    • (2023)Multi-task Ordinal Regression with Labeled and Unlabeled DataInformation Sciences10.1016/j.ins.2023.119669(119669)Online publication date: Sep-2023
    • (2021)Semisupervised Ordinal Regression Based on Empirical Risk MinimizationNeural Computation10.1162/neco_a_0144533:12(3361-3412)Online publication date: 12-Nov-2021
    • (2018)Dimensionality Reduction in Multiple Ordinal RegressionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2017.275200329:9(4088-4101)Online publication date: Sep-2018
    • (2018)High-Order Tensor Regularization with Application to Attribute Ranking2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2018.00457(4349-4357)Online publication date: Jun-2018
    • (2018)HyperSSRNeurocomputing10.1016/j.neucom.2016.05.085274:C(50-57)Online publication date: 24-Jan-2018
    • (2016)A Hybrid Distance Metric Learning for Image RankingProceedings of the Third International Symposium on Computer Vision and the Internet10.1145/2983402.2983442(136-140)Online publication date: 21-Sep-2016
    • (2016)AKSDA-MSVMProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2967263(461-465)Online publication date: 1-Oct-2016
    • (2016)A Maximum Margin Approach for Semisupervised Ordinal Regression ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2015.243496027:5(1003-1019)Online publication date: May-2016
    • (2016)Ordinal Regression Methods: Survey and Experimental StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.245791128:1(127-146)Online publication date: 1-Jan-2016
    • Show More Cited By

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