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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳定立(Ting-Li Chen) | |
dc.contributor.author | Wen-Shao He | en |
dc.contributor.author | 何文劭 | zh_TW |
dc.date.accessioned | 2021-06-17T00:43:13Z | - |
dc.date.available | 2021-02-10 | |
dc.date.copyright | 2020-02-10 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-02-05 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66560 | - |
dc.description.abstract | 線性判別分析可最大程度地提高組間差異與組內差異的比率,它被廣泛用於監督維度縮減中。在傳統的線性判別分析中,判別空間會被標籤錯誤的數據嚴重影響。為了克服這個問題,我們提出了基於伽馬散度的穩健線性判別分析。本文將介紹伽馬線性判別分析算法,並透過影響函數分析其穩健性。我們也藉由模擬資料與人臉辨識資料來展現新方法的優越性。 | zh_TW |
dc.description.abstract | Linear discriminant analysis (LDA) which maximizes the ratio of the between-class variance to the within-class variance is widely used in supervised dimension reduction. In the traditional LDA, the discriminant space can be badly affected by the mislabeled data. To overcome this issue, we propose a robust linear discriminant analysis based on the γ-divergence which is a more robust measure than the Kullback-Leibler divergence. In this thesis, we will introduce the γ-LDA algorithm and analyze its robustness by the influence function. Furthermore, we will show the superior performance of γ-LDA on the simulated examples as well as face image data. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T00:43:13Z (GMT). No. of bitstreams: 1 ntu-109-R07246009-1.pdf: 3115195 bytes, checksum: 6153c7978f698b6983e48f1feee29fdd (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | Acknowledgements i
Abstract ii 1 Introduction 1 1.1 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Robust Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . 3 2 Robustness of Linear Discriminant Analysis 4 2.1 Robust Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 Measurement of Robustness . . . . . . . . . . . . . . . . . . . 5 2.1.2 M-estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Robustness of Linear Discriminant Analysis . . . . . . . . . . . . . . 8 3 The Minimum γ-Divergence Estimation 12 4 γ-LDA Algorithm 16 4.1 Model Specification and Estimation . . . . . . . . . . . . . . . . . . . 17 4.2 Plug-in γ-LDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Projection Pursuit γ-LDA . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3.2 Projection Pursuit . . . . . . . . . . . . . . . . . . . . . . . . 21 4.4 Simulation and Compression of γ-LDA . . . . . . . . . . . . . . . . . 23 5 Robustness of γ-LDA 29 6 Real Data 32 7 Discussion and Future Work 34 | |
dc.language.iso | en | |
dc.title | 使用伽馬散度之穩健線性判別分析法 | zh_TW |
dc.title | Robust linear discriminant analysis based on γ-divergence | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳素雲(Su-Yun Huang),杜憶萍(I-Ping Tu),王偉仲(Wei-Chung Wang) | |
dc.subject.keyword | 穩健統計學,線性判別分析,降維,γ-散度,影響函數, | zh_TW |
dc.subject.keyword | Robust statistics,Linear discriminant analysis,Dimension reduction,γ-divergence,Influence function, | en |
dc.relation.page | 38 | |
dc.identifier.doi | 10.6342/NTU201901737 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-02-06 | |
dc.contributor.author-college | 理學院 | zh_TW |
dc.contributor.author-dept | 應用數學科學研究所 | zh_TW |
顯示於系所單位: | 應用數學科學研究所 |
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