  We extend a well-known dimension reduction method, t-distributed stochastic
neighbor embedding (t-SNE), from non-parametric to parametric by training
neural networks. The main advantage of a parametric technique is the
generalization of handling new data, which is particularly beneficial for
streaming data exploration. However, training a neural network to optimize the
t-SNE objective function frequently fails. Previous methods overcome this
problem by pre-training and then fine-tuning the network. We found that the
training failure comes from the gradient exploding problem, which occurs when
data points distant in high-dimensional space are projected to nearby embedding
positions. Accordingly, we applied the gradient clipping method to solve the
problem. Since the networks are trained by directly optimizing the t-SNE
objective function, our method achieves an embedding quality that is compatible
with the non-parametric t-SNE while enjoying the ability of generalization. Due
to mini-batch network training, our parametric dimension reduction method is
highly efficient. We further extended other non-parametric state-of-the-art
approaches, such as LargeVis and UMAP, to the parametric versions. Experiment
results demonstrate the feasibility of our method. Considering its
practicability, we will soon release the codes for public use.
