加权
特征选择
人工智能
判别式
模式识别(心理学)
维数之咒
特征(语言学)
回归
计算机科学
降维
趋同(经济学)
增广拉格朗日法
拉格朗日乘数
机器学习
统计
数学
数据挖掘
算法
数学优化
医学
放射科
哲学
经济
经济增长
语言学
作者
Xia Wu,Xueyuan Xu,Jianhong Liu,Hailin Wang,Bin Hu,Feiping Nie
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2021-05-01
卷期号:32 (5): 1831-1838
被引量:41
标识
DOI:10.1109/tnnls.2020.2991336
摘要
Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objective function can be solved by employing generalized power iteration and augmented Lagrangian multiplier methods. Experimental results show that the proposed method can more effectively reduce feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is also proved. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.
科研通智能强力驱动
Strongly Powered by AbleSci AI