Unsupervised Spectral Feature Selection With Dynamic Hyper-Graph Learning
Unsupervised Spectral Feature Selection With Dynamic Hyper-Graph Learning
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DOI：
10.1109/tkde.2020.3017250
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发表时间：
2022-06
影响因子：
8.9
通讯作者：
Xiaofeng Zhu;Shichao Zhang;Yonghua Zhu;Pengfei Zhu;Yue Gao
中科院分区：
文献类型：
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作者：
Xiaofeng Zhu;Shichao Zhang;Yonghua Zhu;Pengfei Zhu;Yue Gao
无监督谱特征选择（USFS）方法通过在稀疏特征选择框架中嵌入拉普拉斯正则化算子来保持训练样本的局部相似性，可以输出具有可解释性和区分性的结果。为此，USFS方法通常使用原始数据上的一般图或超图来构造拉普拉斯矩阵。一般情况下，一般图可以度量两个样本之间的关系，而超图可以度量不少于两个样本之间的关系。显然，一般图是超图的一种特殊情况，超图可以比一般图捕捉到更复杂的样本结构。然而，在以前的USFS方法中，拉普拉斯矩阵的构造与特征选择过程分离。此外，原始数据通常包含噪声。每一个都使得输出可靠的特征选择模型变得困难。本文在稀疏特征选择框架下，提出了一种基于超图动态构造拉普拉斯矩阵的特征选择方法。在真实的数据集上的实验结果表明，该方法在聚类和分割任务方面都优于现有的方法。
Unsupervised spectral feature selection (USFS) methods could output interpretable and discriminative results by embedding a Laplacian regularizer in the framework of sparse feature selection to keep the local similarity of the training samples. To do this, USFS methods usually construct the Laplacian matrix using either a general-graph or a hyper-graph on the original data. Usually, a general-graph could measure the relationship between two samples while a hyper-graph could measure the relationship among no less than two samples. Obviously, the general-graph is a special case of the hyper-graph and the hyper-graph may capture more complex structure of samples than the general graph. However, in previous USFS methods, the construction of the Laplacian matrix is separated from the process of feature selection. Moreover, the original data usually contain noise. Each of them makes difficult to output reliable feature selection models. In this paper, we propose a novel feature selection method by dynamically constructing a hyper-graph based Laplacian matrix in the framework of sparse feature selection. Experimental results on real datasets showed that our proposed method outperformed the state-of-the-art methods in terms of both clustering and segmentation tasks.