Файл:Visualizing large-scale and high-dimensional data 1602.00370v2.pdf

Jian Tang1, Jingzhou Liu2∗, Ming Zhang2, Qiaozhu Mei3 1Microsoft Research Asia, jiatang@microsoft.com 2Peking University, {liujingzhou, mzhang_cs}@pku.edu.cn 3University of Michigan, qmei@umich.edu

ABSTRACT

We study the problem of visualizing large-scale and highdimensional 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-ofthe-art methods such as the t-SNE from scaling to largescale 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 tSNE, 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 highdimensional 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.

GeneralTerms Algorithms, Experimentation

Keywords Visualization, big data, high-dimensional data