计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 191-197.doi: 10.11896/jsjkx.210300034
李宗然1, 陈秀宏1,2, 陆赟1, 邵政毅1
LI Zong-ran1, CHEN XIU-Hong1,2, LU Yun1, SHAO Zheng-yi1
摘要: 常见的无监督特征选择方法考虑的只是选择具有判别性的特征,而忽略了特征的冗余性,并且没有考虑到小类问题,故而影响到分类性能。基于此背景,提出鲁棒不相关回归算法。首先,对不相关回归进行研究,使用不相关正交约束,以便找出不相关但具有判别性的特征,不相关约束使得数据结构保持在Stiefel流形中,使模型具有封闭解,避免了传统的岭回归模型引发的可能的平凡解。其次,损失函数与正则化项使用L2,1范数,保证模型的鲁棒性,得到具有稀疏性的投影矩阵;同时将小类问题考虑进去,使投影矩阵数量不受类别数的限制,得到足够多的投影矩阵,从而提升模型的分类性能。理论分析和多个数据集上的实验结果表明,所提出的方法比其他特征选择方法具有更好的性能。
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