swMATH ID: 43966
Software Authors: Yang, Zhirong; Chen, Yuwei; Sedov, Denis; Kaski, Samuel; Corander, Jukka
Description: Stochastic cluster embedding. 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 clusters compared with the state-of-the-art NE approaches. The code of our method is publicly available at https://github.com/rozyangno/sce.
Homepage: https://arxiv.org/abs/2108.08003
Source Code:  https://github.com/rozyangno/sce
Keywords: clustering; information divergence; neighbor embedding; nonlinear dimensionality reduction; stochastic optimization; visualization
Related Software: LargeVis; largeVis; UMAP; t-SNE; DeepView
Cited in: 1 Document

Standard Articles

1 Publication describing the Software, including 1 Publication in zbMATH Year
Stochastic cluster embedding. Zbl 1499.62040
Yang, Zhirong; Chen, Yuwei; Sedov, Denis; Kaski, Samuel; Corander, Jukka

Cited in 1 Serial

1 Statistics and Computing

Cited in 1 Field

1 Statistics (62-XX)

Citations by Year