NCVis: Noise Contrastive Approach for Scalable Visualization

• URL: http://arxiv.org/abs/2001.11411v1
• Date: Thu, 30 Jan 2020 15:43:50 GMT
• Title: NCVis: Noise Contrastive Approach for Scalable Visualization
• Authors: Aleksandr Artemenkov and Maxim Panov
• Abstract summary: NCVis is 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.
• Score: 79.44177623781043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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