关键词: t-SNE算法, 混合属性数据, 降维, 可视化

Abstract:

Aiming at the problem that the traditional t-SNE algorithm can only deal with single attribute data and can’t handle mixed type data very well. An extended t-SNE dimensionality reduction visualization algorithm named E-t-SNE is proposed. The extension facilitates to handle mixed type data. The concept of information entropy is introduced to construct the distance matrix of categorical data. The distance matrix of mixed type data is constructed by combining the distance between categorical data and the Euclidean distance of numerical data. The combined matrix is used into t-SNE algorithm to reduce the dimension and display it in two-dimensional space. In addition, in order to verify the effectiveness of the algorithm, [k]-Nearest Neighbor[(kNN)] algorithm is used to evaluate. Experiments on UCI datasets show that this method not only has good visualization ability in dealing with mixed attribute data, but also can effectively reduce the dimension of different classes of data and improve the classification accuracy of subsequent classifiers.

Key words: t-SNE algorithm, mixed type data, dimension reduction, visualization