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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Generalized Uncorrelated Regression with Adaptive Graph for Unsupervised Feature Selection
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Generalized Uncorrelated Regression with Adaptive Graph for Unsupervised Feature Selection

机译：自适应图的广义不相关回归在无监督特征选择中的应用

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摘要

Unsupervised feature selection always occupies a key position as a preprocessing in the tasks of classification or clustering due to the existence of extra essential features within high-dimensional data. Although lots of efforts have been made, the existing methods neglect to consider the redundancy of features, and thus select redundant features. In this brief, by virtue of a generalized uncorrelated constraint, we present an improved sparse regression model [generalized uncorrelated regression model (GURM)] for seeking the uncorrelated yet discriminative features. Benefited from this, the structure of data is kept in the Stiefel manifold, which avoids the potential trivial solution triggered by a conventional ridge regression model. Besides that, the uncorrelated constraint equips the model with the closed-form solution. In addition, we also incorporate a graph regularization term based on the principle of maximum entropy into the GURM model (URAFS), so as to embed the local geometric structure of data into the manifold learning. An efficient algorithm is designed to perform URAFS by virtue of the existing generalized powered iteration method. Extensive experiments on eight benchmark data sets among seven state-of-the-art methods on the task of clustering are conducted to verify the effectiveness and superiority of the proposed method.
机译：由于在高维数据中存在额外的基本特征，因此在分类或聚类任务中，无监督特征选择始终占据关键位置，作为预处理。尽管已经做了很多努力，但是现有方法忽略了考虑特征的冗余，因此选择了冗余特征。在本文中，借助广义的不相关约束，我们提出了一种改进的稀疏回归模型[广义不相关回归模型（GURM）]，用于寻找不相关但具有判别力的特征。受益于此，数据结构保留在Stiefel流形中，从而避免了由传统的岭回归模型触发的潜在平凡解决方案。除此之外，不相关的约束为模型配备了封闭形式的解决方案。此外，我们还将基于最大熵原理的图正则化项合并到GURM模型（URAFS）中，以便将数据的局部几何结构嵌入流形学习中。借助于现有的广义动力迭代方法，设计了一种有效的算法来执行URAFS。在聚类任务的七个最新方法中，对八个基准数据集进行了广泛的实验，以验证该方法的有效性和优越性。

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  • 作者

    Li Xuelong; Zhang Han; Zhang Rui; Liu Yun; Nie Feiping;

  • 作者单位

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Ctr OPTical IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Ctr OPTical IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China;

    Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPTical IMagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China|Chinese Acad Sci, Haixi Inst, Quanzhou Inst Equipment Mfg, Quanzhou 362000, Peoples R China;

    Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA;

    Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China|Northwestern Polytech Univ, Ctr OPTical IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Generalized uncorrelated constraint; maximum entropy; regression model; unsupervised feature selection;

    机译：广义不相关的约束;最大熵;回归模型;无监督的功能选择;

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