Visualization of disease relationships by multiple maps t-SNE regularization based on Nesterov accelerated gradient

From a biological standpoint, due to the special combination of complex symptoms, some type of complex diseases is difficult to be accurately diagnosed. Known as phenotypic overlap, these sets of disease-related symptoms reveal a common pathological and physiological mechanism. Researchers attempt to visualize the phenotypic relationships between different human diseases from the perspective of machine learning, but traditional methods of visualizing high-dimensional data objects into low-dimensional would be subject to fundamental limitations of metric spaces. Our method is primarily based on the multiple maps t-SNE regularization, which is a probabilistic method for visualizing data points in multiple low-dimensional spaces. We use the Nesterov accelerated gradient method to learn the objective loss function. This method thought to counterweigh too high velocities by “peeking ahead” actual objective values in the candidate search direction, thus providing a larger and timelier correction to velocity. Experiments results on several dataset show that the proposed method outperforms the original version of mm-tSNE and mm-tSNE with regularization, as measured by the neighborhood preservation ratio. This suggests the modified mm-tSNE regularization can be applied directly in other domain including social and biological datasets.