Dynamic graph learning for spectral feature selection
Dynamic graph learning for spectral feature selection
复制标题
用于光谱特征选择的动态图学习
DOI：
10.1007/s11042-017-5272-y
复制
发表时间：
2018-11-01
影响因子：
3.6
通讯作者：
Lei, Cong
中科院分区：
文献类型：
--
作者：
Zheng, Wei;Zhu, Xiaofeng;Lei, Cong
以往的谱特征选择方法在生成相似图时忽略了原始特征空间的噪声和冗余的负面影响，忽略了图矩阵学习与特征选择之间的关联，容易产生次优结果。为了解决这些问题，本文将图学习和特征选择结合在一个框架中，以获得最优的选择性能。更具体地说，我们使用最小二乘损失函数和1,2范数正则化来去除噪声和冗余特征的影响，并使用所得到的特征之间的局部相关性从原始数据的低维空间动态学习图矩阵。在真实数据集上的实验结果表明，我们的方法在分类任务中优于最先进的特征选择方法。
Previous spectral feature selection methods generate the similarity graph via ignoring the negative effect of noise and redundancy of the original feature space, and ignoring the association between graph matrix learning and feature selection, so that easily producing suboptimal results. To address these issues, this paper joints graph learning and feature selection in a framework to obtain optimal selected performance. More specifically, we use the least square loss function and anℓ2,1-norm regularization to remove the effect of noisy and redundancy features, and use the resulting local correlations among the features to dynamically learn a graph matrix from a low-dimensional space of original data. Experimental results on real data sets show that our method outperforms the state-of-the-art feature selection methods for classification tasks.