隨著網際網路以及其應用程式普及，資料和資訊量也快速的成長。海量資料研究成為現今資訊工程學科的主要研究題目。而資料視覺化研究能幫助我們以直觀的方式處理海量資料。
在本篇論文中應用了數據降維方法來投射原始資料到較低維度的子空間，這些方法能以可接受的少量訊息遺失來保存大部分的原始資料特徵。主成份分析 (PCA) 和線性識別分析 (LDA) 是兩種常見已被證實能有效分類的線性投射方法；而獨立成份分析 (ICA) 原本是為了解決盲訊號分離問題而提出的，同為線性投射方法，獨立成份分析也能計算投影矩陣並映射資料到子空間。與主成份分析和線性識別分析不同的是：獨立成份分析將原始資料視作一個訊號混合體，要將呈現非高斯分布的原始訊號源分別擷取出來。
在資料視覺化的實作過程中，我們將維度降為2來讓資料投射到歐基里德平面上。實驗結果表示線性識別分析的成果比主成份分析來得好，而獨立成份分析因為存在隨機因素，所以每次的產出結果並不一致。
藉由投射到低維度的資料視覺化，我們可以想像或展示高維度資料的結構，例如群聚特徵。

While the internet applications are widely used, the amount of data information has a rapid growth. Big data analysis has become a popular study issue nowadays. Data visualization could help us deal with these big data in an intuitive way.
We apply linear dimensionality reduction methods to project the observation data into lower-dimensional subspace. The method could preserve most of the data characteristic with omitting an acceptable little information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two common linear mapping methods which are proved to have an effective classification result. Independent Component Analysis (ICA) is originally proposed to solve the blind source separation problem. Same as a linear mapping method, ICA also computes a mapping matrix for data projection. But unlike PCA and LDA, ICA assumes non-Gaussian distributions of data could separate the original sources from a mixture.
In the implementation of data visualization, we reduce the dimensionality to 2 in order to present the projected data on Euclidean geometry. The experiment result shows that LDA has a better data classification performance than PCA. An ICA algorithm has random factors in itself which may lead to sundry results.
By means of low dimensional data visualization, one can imagine or reveal the structure of high dimensional data, for example, the characteristic of clustering.

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