基于图谱的多标记特征选择算法OA北大核心CSCDCSTPCD
Spectral Theory Based Multi-Label Feature Selection
特征选择在传统的单标记问题中已经得到深入的研究，但是大多数传统的特征选择算法却无法用于多标记问题。这是因为多标记问题中的每一个数据样本都同时与多个类标相关联，此时需要设计新的指标来评价特征。并且由于多个类标之间通常存在一定的关联性，在设计特征选择算法时还需要对类标的结构进行建模以利用类标的关联信息。采用谱特征选择（spectral feature selection，SPEC）框架解决上述问题。SPEC所需的相似性矩阵和图结构由样本类标的Jacc…查看全部>>
Feature selection has been deeply studied in traditional single label problem. When it comes to multi-label problem, most of traditional feature selection algorithms for single label problem are not able to be applied directly, since instances in multi-label problems are associated with several labels simultaneously, new criteria to evaluate features are needed. Because of the correlations among several labels, new methods to model labels structure are neede…查看全部>>
严鹏;李云
南京邮电大学 计算机学院，南京 210023南京邮电大学 计算机学院，南京 210023
计算机与自动化
多标记学习谱特征选择标记关联性
multi-label learningspectral feature selectionlabel correlation
《计算机科学与探索》 2016 (4)
543-553,11
The Natural Science Foundation of Jiangsu Province of China under Grant Nos. BK20131378, BK20140885(江苏省自然科学基金)the Postdoctoral Science Foundation of Jiangsu Province under Grant No.1401045C (江苏省博士后科研资助计划)the Science Foundation of Nanjing University of Posts and Telecommunications under Grant No. NY214034(南京邮电大学科研基金)
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