Multi-view prototype-based disambiguation for partial label learning

Shiding Sun

doi:10.1016/j.patcog.2023.109625

Multi-view prototype-based disambiguation for partial label learning

2023-09-01

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DOI：10.1016/j.patcog.2023.109625

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In this work, we study the multi-view partial label learning (MVPLL) problem, where each instance is depicted by different view features and associated with a set of candidate labels, among which a true label exists but is inaccessible in the training phase. Most existing PLL methods only consider single view case, which learn view classifier independently and neglect the view correlations, thus can not be applied to solve MVPLL problem. Due to the non-deep framework, traditional MVPLL approach is weak in the representation ability, so its performance is still to be improved. To solve the MVPLL problem, a deep multi-view prototype-based disambiguation approach is proposed in this paper. Specifically, we innovatively employ the deep neural network for multi-view ambiguously-labeled image classification to enhance the representation ability, which makes use of the information fusion between multiple views. To improve the discriminative ability, we propose multi-view prototype-based label disambiguation algorithm. On theoretical aspect, an estimation error bound for view-risk estimator is established, which is shown to be larger than that for fuse-risk estimator. Experiments demonstrate the superiorities of our proposed method in terms of the prediction accuracy. & COPY; 2023 Elsevier Ltd. All rights reserved.

Keywords:

Multi-view learning

Partial label learning

Weakly supervised learning

Multi-view prototype-based disambiguation for partial label learning

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