摘要
针对现有无监督属性约简方法只单一使用子空间学习或属性选择的方法,并且忽略数据之间的内在相关性,本文提出一种新的属性选择方法。首先提出一个属性自表达损失函数加上一个稀疏正则化(l2,1-范数)实现无监督学习与属性选择。然后嵌入子空间学习方法,并使用低秩约束和图正则化项考虑数据的全局结构和局部结构。经聚类实验验证,该算法较对比算法能取得更好的效果。
A new method of feature selection is proposed to remedy several drawbacks with the existing unsupervised feature reduction method which only utilize the single subspace approach or feature selection method and ignores the inherent correlation within data.Specifically,a novel feature selfrepresentation loss function is proposed to conduct unsupervised learning and feature selection by combining a sparse regularization(l2,1-norm).And then,subspace learning method and low rank constraint with graph regularization are embeded into the model,to take into account of the global structure and local structure of the data,respectively.Experimental results show that the presented algorithm can achieve better results than some selected comparison algorithms.
引文
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