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Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval

机译：跨模型检索的自适应半监督特征选择

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摘要

Inorder to exploit the abundant potential information of the unlabeled data and contribute to analyzing the correlation among heterogeneous data, we propose the semi-supervised model named adaptive semi-supervised feature selection for cross-modal retrieval. First, we utilize the semantic regression to strengthen the neighboring relationship between the data with the same semantic. And the correlation between heterogeneous data can be optimized via keeping the pairwise closeness when learning the common latent space. Second, we adopt the graph-based constraint to predict accurate labels for unlabeled data, and it can also keep the geometric structure consistency between the label space and the feature space of heterogeneous data in the common latent space. Finally, an efficient joint optimization algorithm is proposed to update the mapping matrices and the label matrix for unlabeled data simultaneously and iteratively. It makes samples from different classes to be far apart, while the samples from same class lie as close as possible. Meanwhile, the l(2,1)-norm constraint is used for feature selection and outlier reduction when the mapping matrices are learned. In addition, we propose learning different mapping matrices corresponding to different sub-tasks to emphasize the semantic and structural information of query data. Experiment results on three datasets demonstrate that our method performs better than the state-of-the-art methods.
机译：InOrder利用未标记数据的丰富潜在信息，并有助于分析异构数据之间的相关性，提出了用于跨模型检索的自适应半监督特征选择的半监督模型。首先，我们利用语义回归来加强与相同语义的数据之间的相邻关系。在学习共同的潜在空间时，可以通过保持成对近距离来优化异构数据之间的相关性。其次，我们采用基于图形的约束来预测用于未标记数据的准确标签，并且还可以在标签空间和共同潜在空间中的异构数据的特征空间之间保持几何结构一致性。最后，提出了一种有效的关节优化算法以同时和迭代地更新映射矩阵和标签矩阵。它使不同类别的样本相距较远，而来自同一类的样本尽可能接近。同时，L（2,1）-norm约束用于在学习映射矩阵时的特征选择和异常值。另外，我们提出了学习对应于不同子任务的不同映射矩形，以强调查询数据的语义和结构信息。三个数据集上的实验结果表明，我们的方法比现有技术的方法更好地执行。

著录项

  • 来源
    《IEEE transactions on multimedia》 |2019年第5期|1276-1288|共13页
  • 作者

    Yu En; Sun Jiande; Li Jing; Chang Xiaojun; Han Xian-Hua; Hauptmann Alexander G.;

  • 作者单位

    Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China;

    Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China;

    Shandong Management Univ Sch Mech & Elect Engn Jinan 250014 Shandong Peoples R China|Shandong Normal Univ Jinan 250014 Shandong Peoples R China;

    Monash Univ Fac Informat Technol Clayton Vic 3800 Australia;

    Yamaguchi Univ Grad Sch Sci & Technol Innovat Yamaguchi 7538511 Japan;

    Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Semi-supervised; cross-modal retrieval; feature selection;

    机译：半监督;跨模型检索;特征选择;

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