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图学学报

图学学报

• 专论：第十九届全国图象图形学学术会议(NCIG2018) • 上一篇    下一篇

基于结构化多视图稀疏限定的 监督特征选择算法研究

  

  1. 华北理工大学，河北 唐山 063210
  • 出版日期:2018-12-31 发布日期:2019-02-20
  • 基金资助:
    国家自然科学基金项目(61502143)；河北省自然科学基金面上项目(F2016209165)；华北理工大学杰出青年基金项目(JQ201715)；华北理工 大学博士科研启动基金(201510)；河北省高等学校科学技术研究项目(QN2018115)

Research of Supervised Feature Selection Algorithm Based on  Structured Multi-View Sparse Regularization

  1. North China University of Science and Technology, Tangshan Hebei 063210, China
  • Online:2018-12-31 Published:2019-02-20

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摘要： 为了有效利用多视图数据信息提升监督特征选择的性能，构建了一种结构化多视 图稀疏限定，并基于该稀疏限定提出了一种监督特征选择方法，即结构化多视图监督特征选择 方法(SMSFS)。该方法在特征选择过程中能够同时考虑不同视图特征的重要性以及同一视图中 不同特征的重要性，从而有效的结合多视图数据信息，提升监督特征选择的性能。SMSFS 目标 函数是非凸的，设计了一个有效的迭代算法对目标函数进行求解。将所提结构化多视图监督特 征选择方法 SMSFS 应用到了图像标注任务，在 NUS-WIDE 和 MSRA-MM2.0 图像数据库上进 行了实验，并与其他特征选择算法进行了比较，实验结果表明该算法能够有效结合多视图数据 信息，提升特征选择性能。

关键词: 多视图学习, 结构化稀疏限定, 监督特征选择

Abstract:  In order to effectively utiliz the multi-view data information and enhance the feature selection performance, the structured multi-view sparse regularization was constructed, based on which a novel supervised feature selection method, namely structured multi-view supervised feature selection (SMSFS), was proposed. SMSFS could simultaneously consider the importance of each view features and the importance of individual feature in each view to combine the multi-view data information effectively in the feature selection process, and then to boost the supervised feature selection performance. Because the objective function of SMSFS is non-convex, an effective iterative algorithm was proposed to solve the objective function. The proposed structured multi-view supervised feature selection method SMSFS was applied into image annotation task and extensive experiments were performed on NUS-WIDE and MSRA-MM2.0 image datasets. The proposed method SMSFS was compared with other feature selection methods and the experimental results demonstrated the effectiveness of SMSFS, which means that it could effectively utilize the multi-view data information to boost the feature selection performance.

Key words: multi-view learning, structured sparse regularization, supervised feature selection