摘要
PCA应用广泛,是个有效的、巧妙的降维技术,但是缺乏深刻理解,故对此进行研究。PCA转换消除信号的相关性,较少数据冗余度,转换结果比转换前减少至少一个成分;且能量集中。PCA和多个抽象概念领域有密切关系,因此,需要简单案例来解释才能达到最佳教学效果。
Principle Component Analysis(PCA)is a widely used,effective,and ingenious data transform and dimension reduction method,but often purely understood by students,so it's necessary to explain.PCA transform removes correlation,decreases redundancy by removing at least one dimension,and energy is concentrated.PCA is related with many abstract concepts,thus necessary to be explained with typical examples to have best educational result.
作者
米吉提.阿不里米提
吾米提.尤努斯
艾斯卡尔.艾木都拉
MIJIT Ablimit;UMUT Yunus;ASKAR Hamdulla(College of Information Science and Engineering,Xinjiang University,Urumqi 830046)
基金
国家自然科学基金(No.61462085
No.61662078)
关键词
PCA
案例教学
机器学习
降维
PCA(Principle Component Analysis)
Teaching Case
Machine Learning
Dimension Reduction
