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High dimensional data is difficult to visualize. T-Distributed Stochastic Neighbor Embedding (t-SNE) is a popular technique for dimensionality reduction enabling a planar visualization of a dataset preserving as much as possible its metric. This paper explores the theoretical background of t-SNE and its accelerated version. A comparison of the performance of t-SNE on various datasets with different dimensions is also performed.
Jung et al. (Thu,) studied this question.