We study the problem of visualizing large-scale and high-dimensional data in
a low-dimensional (typically 2D or 3D) space. Much success has been reported
recently by techniques that first compute a similarity structure of the data
points and then project them into a low-dimensional space with the structure
preserved. These two steps suffer from considerable computational costs,
preventing the state-of-the-art methods such as the t-SNE from scaling to
large-scale and high-dimensional data (e.g., millions of data points and
hundreds of dimensions). We propose the LargeVis, a technique that first
constructs an accurately approximated K-nearest neighbor graph from the data
and then layouts the graph in the low-dimensional space. Comparing to t-SNE,
LargeVis significantly reduces the computational cost of the graph construction
step and employs a principled probabilistic model for the visualization step,
the objective of which can be effectively optimized through asynchronous
stochastic gradient descent with a linear time complexity. The whole procedure
thus easily scales to millions of high-dimensional data points. Experimental
results on real-world data sets demonstrate that the LargeVis outperforms the
state-of-the-art methods in both efficiency and effectiveness. The
hyper-parameters of LargeVis are also much more stable over different data
sets.

本文提出了一种叫做 LargeVis 的技术，旨在解决将大规模和高维数据可视化在低维空间的问题。与 t-SNE 相比，LargeVis 构建精确近似的 K 最近邻图的计算成本更低，并采用了一个原则上的概率模型来进行可视化，整个过程易于扩展到数百万个高维数据点。实验结果表明，LargeVis 在效率和效果方面均优于现有的先进方法。