浅谈稀疏主成分分析
A Brief Overview of Sparse Principal Component Analysis
DOI: 10.12677/ORF.2022.122018, PDF,   
作者: 武丽霞：内蒙古大学数学科学学院，内蒙古 呼和浩特
关键词: 稀疏主成分分析；数据降维；人脸识别；Sparse Principal Component Analysis； Data Dimension Reduction； Face Identification
摘要: 传统主成分分析虽然可以有效地降低数据的维度，但是在数据的解释性方面表现不足。为了改进这一不足，稀疏主成分分析应运而生，它将稀疏性融合到了主成分分析中，保持最大方差的同时得到稀疏的载荷向量，可以更好地挖掘数据信息。本文首先对主成分的求解方法做了介绍，然后说明了稀疏主成分分析在主成分分析的基础上进行的方法和理论改进，最后举例说明稀疏主成分分析在人脸识别、海洋油污检测、医学诊断各方面的广泛应用。
Abstract: Although traditional principal component analysis can effectively reduce the dimension of data, it is insufficient in the interpretation of data. In order to improve this deficiency, sparse principal component analysis emerges at the right moment. It integrates sparsity into principal component analysis to obtain sparse load vector while maintaining maximum variance, which can better mine data information. This paper first introduces the solution method of principal component analysis, then explains the method and theoretical improvement of sparse principal component analysis based on principal component analysis, and finally illustrates the wide application of sparse principal component analysis in face recognition, Marine oil pollution detection and medical diagnosis with examples.
文章引用：武丽霞. 浅谈稀疏主成分分析[J]. 运筹与模糊学, 2022, 12(2): 183-190. https://doi.org/10.12677/ORF.2022.122018

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