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
