Multi-view prototype-based disambiguation for partial label learning

Abstract

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.