Abstract
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.