计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 220900003-9.doi: 10.11896/jsjkx.220900003
朱建勇1,2, 李兆祥1,2, 徐彬1,2, 杨辉1,2, 聂飞平3
ZHU Jianyong1,2, LI Zhaoxiang1,2, XU Bin1,2, YANG Hui1,2, NIE Feiping3
摘要: 传统的基于图学习的无监督特征选择算法通常采用稀疏正则化方法来选择特征,但这种方法过于依赖于图学习的效率,并且存在正则化参数调优困难等问题。为解决这些问题,针对性地提出了一种基于图嵌入学习的正交局部保持投影无监督特征选择(Orthogonal Locality Preserving Projection Unsupervised Feature Selection via Graph Embedding,OLPPFS) 算法。首先,利用能够保持数据局部几何流形结构的局部保持投影方法增强数据的线性映射能力,同时约束正交方向投影以方便数据重构;其次,通过图嵌入学习方法快速构建稀疏相似图来描述样本数据的内在结构;接着,采用$\ell$2,0范数约束投影矩阵的值,准确选择指定数目的判别性特征子集;最后,针对$\ell$2,0范数NP难题,设计一种有效求解$\ell$2,0范数问题的无参迭代算法求解该模型。仿真结果表明了所提算法的有效性和优越性。
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