Modern methods for data visualization via dimensionality reduction, such as
t-SNE, usually have performance issues that prohibit their application to large
amounts of high-dimensional data. In this work, we propose NCVis -- a
high-performance dimensionality reduction method built on a sound statistical
basis of noise contrastive estimation. We show that NCVis outperforms
state-of-the-art techniques in terms of speed while preserving the
representation quality of other methods. In particular, the proposed approach
successfully proceeds a large dataset of more than 1 million news headlines in
several minutes and presents the underlying structure in a human-readable way.
Moreover, it provides results consistent with classical methods like t-SNE on
more straightforward datasets like images of hand-written digits. We believe
that the broader usage of such software can significantly simplify the
large-scale data analysis and lower the entry barrier to this area.

提出了一种基于噪声对比估计的高性能降维方法 NCVis，其在速度和表现质量方面优于现有技术，在处理自然语言的大数据集时速度也很快，可极大简化大规模数据分析并降低该领域的入门门槛。