| 引用本文: | 宋 雨,许王琴,李荣鹏,宋学力,肖玉柱.基于自适应流形正则化自表示的无监督特征选择算法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2023，40（6）：44-52 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 针对基于流形正则化自表示(MRSR)的无监督特征选择算法直接从原始的样本空间构造相似矩阵可能会
导致重构空间中样本的相似性描述得不够准确的问题,提出了基于自适应流形正则化自表示的无监督特征选择
(AMRSR)算法。 基于自适应流形正则化自表示的无监督特征选择算法在 MRSR 算法的基础上通过对相似矩阵施
加概率最近邻约束将相似矩阵的学习嵌入到优化过程中,在重构空间中自适应地学习样本的相似性,使得在每一
次迭代中获取更加精确的样本局部几何流形结构,从而选择具有代表性且保持局部几何流形结构的特征。 最后,
在四个公开数据集上进行了大量的对比实验,通过将算法的特征选择结果用于 K-means 聚类并采取两种常见的聚
类评价指标:聚类精确度和归一化互信息评价聚类效果。 实验结果表明,AMRSR 算法与现有的一些算法相比有更
高的聚类精确度和归一化互信息,进一步表明该算法特征选择效果更好。 |
| 关键词: 无监督特征选择 自表示 流形正则化 自适应 相似矩阵 |
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SONG Yu， XU Wangqin， LI Rongpeng， SONG Xueli， XIAO Yuzhu
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School of Science Chang?? an University Xi?? an 710064 China
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| Abstract: |
| Unsupervised feature selection algorithm based on manifold regularization self-representation MRSR directly
constructed similarity matrix from the original sample space which might lead to inaccurate similarity description of
samples in the reconstructed space. To solve this problem an unsupervised feature selection algorithm based on adaptive
manifold regularization self-representation AMRSR was proposed. On the basis of MRSR algorithm unsupervised
feature selection algorithm based on adaptive manifold regularization self-representation embedded the learning of similar
matrix into the optimization process by imposing probabilistic nearest neighbor constraints on similar matrix and adaptively
learned the similarity of samples in the reconstructed space so that more accurate local geometric manifold structure of
samples could be obtained in each iteration and then representative features with local geometric manifold structure could
be selected. Finally a large number of comparative experiments were carried out on four public datasets. By applying the
feature selection results of the algorithms to K-means clustering two common clustering evaluation indexes were adopted
clustering accuracy and normalized mutual information to evaluate the clustering effect. Experimental results show that the
AMRSR algorithm has higher clustering accuracy and normalized mutual information than some existing algorithms which
further indicates that the feature selection effect of this algorithm is better. |
| Key words: unsupervised feature selection self-representation manifold regularization adaptation similar matrix |
