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Visualization of non-metric relationships by adaptive learning multiple maps t-SNE regularization
Conference proceeding

Visualization of non-metric relationships by adaptive learning multiple maps t-SNE regularization

Xianjun Shen, Xianchao Zhu, Xingpeng Jiang, Li Gao, Tingting He and Xiaohua Hu
2017 IEEE International Conference on Big Data (Big Data), v 2018-, pp 3882-3887
Dec 2017
DOI: https://doi.org/10.1109/BigData.2017.8258393

Abstract

Acceleration adaptative l earning method Data visualization Diseases Error analysis Extraterrestrial measurements Nesterov momentum peeking ahead phenotypic visualization RMSProp Visualization Machine Learning
Known as phenotypic overlapping, some disease-rel ated symptoms share a common pathologi cal and physiological mechanism. Researchers attempt to visualize the phenotypic relationships between different human diseases from the perspective of machine learning, but traditional visualization methods may be subject to fundamental limitations of metric spaces. Multiple maps t-SNE regularization method, a probabilistic method for visualizing data points in multiple low-dimensional spaces has been proposed to address the limitation. However, the convergence speed is low when apply on the scale dataset. We use the RMSProp with Nesterov momentum method to learn the objective loss function. This method normalize the gradients by applying an exponential moving average of gradient magnitude for each iteration parameter and use Nesterov momentum to counterweigh too high velocities by "peeking ahead" actual objective values in the candidate search direction. This method convergent faster than the original method of convergence speed. Experiments results on several dataset shows that the proposed method outperforms the several version of mm-tSNE with or without regularization, as measured by the neighborhood preservation ratio and error rate. This suggests the modified mm-tSNE regularization can be applied directly in other domain including social, biological and microbiomic datasets.

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4 citations in Web of Science
10 citations in Scopus

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