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PCA in Frequency Domain : 주파수 영역에서의 주성분 분석

DC Field Value Language
dc.contributor.advisor오희석-
dc.contributor.author조유정-
dc.date.accessioned2017-07-19T08:46:22Z-
dc.date.available2017-07-19T08:46:22Z-
dc.date.issued2016-02-
dc.identifier.other000000132869-
dc.identifier.urihttps://hdl.handle.net/10371/131310-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 2. 오희석.-
dc.description.abstractPrincipal component analysis (PCA) is one of dimension reduction techniques
in data analysis. By using PCA, large dimensional data can be reduced to
small dimensional one. Then, it is helpful for data summary and data visual-
ization. In this paper, we analyze principal components in frequency domain
and compare PCA results in time domain and frequency domain using time
series data.		-
dc.description.tableofcontentsChapter 1 Introduction 1

Chapter 2 Review of PCA in Frequency Domain 3
2.1. Principal Components Analysis 3
2.2. Principal Component Series 5

Chapter 3 Data analysis 8
3.1. Data Summary 8
3.2. Compare PCA Results 9
3.3. Detection of Common Signals of PM-10 Data 14

Chapter 4 Conclusion 18

References 19

국문초록 20		-
dc.formatapplication/pdf-
dc.format.extent2825704 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectFrequency domain-
dc.subjectPrincipal component-
dc.subjectPrincipal component analysis-
dc.subjectTime series data-
dc.subject.ddc519-
dc.titlePCA in Frequency Domain-
dc.title.alternative주파수 영역에서의 주성분 분석-
dc.typeThesis-
dc.contributor.AlternativeAuthorJo Youjung-
dc.description.degreeMaster-
dc.citation.pages20-
dc.contributor.affiliation자연과학대학 통계학과-
dc.date.awarded2016-02-
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