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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/92269


    Title: Principal Components on t-SNE
    Authors: 傅維康;Fu, Connor Wei
    Contributors: 統計研究所
    Keywords: 高維度資料;解釋性;主成分分析;t-隨機鄰近嵌入法;視覺化
    Date: 2023-07-26
    Issue Date: 2023-10-04 15:24:46 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 在眾多視覺化方法中,t-隨機鄰近嵌入法 (t-SNE) 是相當有效且被廣泛使用的
    技術之一。視覺上,t-SNE 有能力在 2 維或 3 維空間中呈現高維度資料集的結
    構,然而,對資料進一步的解釋能力較弱。相對地,主成分分析 (PCA) 具有足
    夠的解釋性,但視覺化效果較差。在本文中,我們提出一套新的方法。過程將
    t-SNE 與 PCA 的概念做結合,旨在保留良好視覺化結果的同時,也提升資料的
    解釋力。透過尋找與 t-SNE 分群相關的特徵,我們能夠得到用來解釋 t-SNE 映
    射的主成分 (principal component)。這種方法除了提高 t-SNE 的解釋性以及應用
    價值,也為資料視覺化研究提供了新的思路。在數值研究中,我們透過提出的
    方法以及 PCA 方法獲得主成分進行資料降維,再重新執行 t-SNE 演算法進行視
    覺化。視覺化的重建結果顯示,PCA 所找到的主成分無法有效還原 t-SNE 的映
    射,而我們的方法不僅能夠重新還原,甚至能提供更優秀的視覺化效果。;t-distributed stochastic neighbor embedding (t-SNE) is one of highly effective and
    widely used visualization methods. It is capable to visualize the structure of high dimensional data by giving each datapoint a location in a 2D or 3D map. However,
    it lacks further interpretability of data. On the other hand, principal component analysis
    (PCA) provides sufficient interpretability but yields inferior visualization. In this paper,
    we propose a novel approach that combines the concepts of t-SNE and PCA to preserve
    good visualizing results while keeping the interpretability of data. By searching for fea tures that are correlated with the clustering performed by t-SNE, we can obtain dedicated
    principal components for t-SNE. This method not only improves the interpretability and
    applicability of t-SNE but also provides new insights for data visualization research. In
    our numerical study, we use the principal components from our method and PCA method
    to reapply the t-SNE algorithm for visualization. The reconstructed results demonstrate
    that the principal components identified by PCA fail to effectively reproduce the map pings of t-SNE, while our method not only achieves successful reconstruction but also
    offers superior visualization outcomes
    Appears in Collections:[統計研究所] 博碩士論文

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