Zhao Xiaowei, Ma Zhigang, Li Zhi, Li Zhihui
School of Information Science and Technology, Northwest University, Xian, Shaanxi 710769, China
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.
Neural Comput. 2018 Feb;30(2):526-545. doi: 10.1162/neco_a_01036. Epub 2017 Nov 21.
In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.
近年来，多标签分类在多媒体标注中受到了广泛关注。然而，大多数多标签分类方法仅关注多个标签和概念之间存在的内在相关性，而忽略了特征与目标概念之间的相关性。为了获得更稳健的多标签分类结果，我们提出了一种新的多标签分类方法，旨在通过利用超图来捕捉多个概念之间的相关性，超图已被证明对关系学习有益。此外，我们考虑挖掘特征-概念相关性，这在许多多标签学习算法中常常被忽视。为了更好地展示特征-概念相关性，我们对所提出的方法施加了稀疏性约束。我们将所提出的方法与其他几种多标签分类方法进行比较，并通过在几个数据集上的平均精度均值来评估分类性能。实验结果表明，所提出的方法优于现有方法。
