T-SNE Is Not Optimized to Reveal Clusters in Data

The rapid growth in the amount of data processed by analysts demands more efficient information digestion and communication methods. Data visualization by dimensionality reduction facilitates a viewer to digest information in massive data sets quickly. Therefore, it is increasingly applied as a critical component in scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, drug discovery, etc. Stochastic Neighbor Embedding (SNE) [4] is a widely used nonlinear dimensionality reduction (NLDR) method, which approximately preserves the pairwise probabilities of being neighbors (neighboring probabilities for short) in the input space. In particular, the Student t-Distributed Stochastic Neighbor Embedding (t-SNE) [9] has become one of the most popular nonlinear dimensionality reduction methods for data visualization. The t-SNE method employs a heavy-tailed distribution for the neighboring probabilities in the embedding and minimizes their Kullback-Leibler divergence against the precomputed input probabilities.