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Title: | 使用伽馬散度之穩健線性判別分析法 Robust linear discriminant analysis based on γ-divergence |
Authors: | Wen-Shao He 何文劭 |
Advisor: | 陳定立(Ting-Li Chen) |
Keyword: | 穩健統計學,線性判別分析,降維,γ-散度,影響函數, Robust statistics,Linear discriminant analysis,Dimension reduction,γ-divergence,Influence function, |
Publication Year : | 2020 |
Degree: | 碩士 |
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。 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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66560 |
DOI: | 10.6342/NTU201901737 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 應用數學科學研究所 |
Files in This Item:
File | Size | Format | |
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ntu-109-1.pdf Restricted Access | 3.04 MB | Adobe PDF |
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