特征选择
计算机科学
模式识别(心理学)
人工智能
非负矩阵分解
矩阵分解
选择(遗传算法)
因式分解
基质(化学分析)
特征(语言学)
数学
算法
特征向量
化学
物理
哲学
量子力学
色谱法
语言学
作者
Miao Qi,Ting Wang,Fucong Liu,Baoxue Zhang,Jianzhong Wang,Yugen Yi
标识
DOI:10.1016/j.neucom.2017.08.047
摘要
Abstract Feature selection is an interesting and challenging task in data analysis process. In this paper, a novel algorithm named Regularized Matrix Factorization Feature Selection (RMFFS) is proposed for unsupervised feature selection. Compared with other matrix factorization based feature selection methods, a main advantage of our algorithm is that it takes the correlation among features into consideration. Through introducing an inner product regularization into our algorithm, the features selected by RMFFS would not only well represent the original high-dimensional data, but also contain low redundancy. Moreover, a simple yet efficient iteratively updating algorithm is also developed to solve the proposed RMFFS. Extensive experimental results on nine real world databases demonstrate that our proposed method can achieve better performance than some state-of-the-art unsupervised feature selection methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI