基于Hessian正则的自适应损失半监督特征选择
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华东交通大学

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TP391

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国家自然科学基金地区项目(61563015,61963015, 61863014),国家自然科学基金重点项目(61733005 ),江西省自然科学基金项目(20171ACB21039,20192BAB207024) ,江西省教育厅科技项目(GJJ150552,GJJ170376)


Adaptive Loss Semi-supervised Feature Selection based on Hessian Regularization
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East China Jiaotong University

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    摘要:

    基于拉普拉斯图的半监督特征选择算法处理高维、少标签样本缺乏外推能力及鲁棒性差等特点,本文提出一种基于Hessian正则的自适应损失半监督稀疏特征选择算法.首先,为提升线性映射能力,利用Hessian正则保留数据的局部流形结构;其次,为增强模型对具有较小或者较大损失数据的鲁棒性,引入自适应损失函数,通过调节自适应损失函数的参数确定最小损失;接着,引入l2,p范数稀疏投影矩阵,提升特征的区分度及增加模型适应度;最后采用递归迭代优化求解目标函数.仿真实验表明所提方法的有效性和优越性.

    Abstract:

    Since the semi-supervised feature selection based on Laplacian graph has received extensive attention. However, due to the lack of extrapolation ability of the Laplacian operator, the limited labeled data is still not well utilized and is too sensitive to outliers. Therefore, an adaptive loss semi-supervised sparse feature selection algorithm based on Hessian regularization is proposed, named AHFS. Firstly, Hessian is used to preserve the local manifold structure of data for improving the linear mapping capability. Then, adaptive loss is integrated to improve the robustness of the model to data with small or large loss. Moreover, l2,p-norm is leveraged to constrain the prediction matrix, which not only improves the distinguishing degree of features, but also increases the adaptability of the proposed model. Then, a recursive iterative optimization algorithm is proposed to solve the proposed model. Finally, a systematic experiments are carried out through real public data sets, and the model is analyzed from several aspects to verify the effectiveness and superiority of the proposed method.

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历史
  • 收稿日期:2019-10-29
  • 最后修改日期:2021-02-19
  • 录用日期:2020-02-29
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