Stochastic Cluster Embedding

Funding Information: This work was supported by The Research Council of Norway, Grant Number 287284, ERC Grant Number 742158, the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI), and UKRI Turing AI World-Leading Researcher Fellowship, EP/W002973/1. We acknowledge for using the IDUN computing cluster (Själander et al. ) provided at Norwegian University of Science and Technology. Neighbor embedding (NE) aims to preserve pairwise similarities between data items and has been shown to yield an effective principle for data visualization. However, even the best existing NE methods such as stochastic neighbor embedding (SNE) may leave large-scale patterns hidden, for example clusters, despite strong signals being present in the data. To address this, we propose a new cluster visualization method based on the Neighbor Embedding principle. We first present a family of Neighbor Embedding methods that generalizes SNE by using non-normalized Kullback–Leibler divergence with a scale parameter. In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE. We also develop an efficient software that employs asynchronous stochastic block coordinate descent to optimize the new family of objective functions. Our experimental results demonstrate that the method consistently and substantially improves the visualization of data clusterscompared with the state-of-the-art NE approaches. The code of our method is publicly available at https://github.com/rozyangno/sce.