Noise-free principal component analysis: An efficient dimension reduction technique for high dimensional molecular data
作者：
Highlights：
• Principal component analysis (PCA) is one of the powerful dimension reduction techniques widely used in data mining field.
• Data usually contaminated by the noise.
• Noise in the data has effect in computation of PC’s components.
• We use regularization method to filter the diffusion of the noise in PC’s.
• Experimental results shows the power of the new approach.
摘要
•Principal component analysis (PCA) is one of the powerful dimension reduction techniques widely used in data mining field.•Data usually contaminated by the noise.•Noise in the data has effect in computation of PC’s components.•We use regularization method to filter the diffusion of the noise in PC’s.•Experimental results shows the power of the new approach.
论文关键词：PCA,Regularization,High-dimensional data analysis,Classification
论文评审过程：Available online 23 June 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.06.024
