AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization

By Cong Fu, Yonghui Zhang, Deng Cai, Xiang Ren. 2019

24 citations recorded by the source corpus.

Visualization of high-dimensional data is a fundamental yet challenging problem in data mining. These visualization techniques are commonly used to reveal the patterns in the high-dimensional data, such as clusters and the similarity among clusters. Recently, some successful visualization tools (e.g., BH-t-SNE and LargeVis) have been developed. However, there are two limitations with them : (1) they cannot capture the global data structure well. Thus, their visualization results are sensitive to initialization, which may cause confusions to the data analysis. (2) They cannot scale to large-scale datasets. They are not suitable to be implemented on the GPU platform because their complex algorithm logic, high memory cost, and random memory access mode will lead to low hardware utilization. To address the aforementioned problems, we propose a novel visualization approach named as Anchor-t-SNE (AtSNE), which provides efficient GPU-based visualization solution for large-scale and high-dimensional data. Specifically, we generate a number of anchor points from the original data and regard them as the skeleton of the layout, which holds the global structure information. We propose a hierarchical optimization approach to optimize the positions of the anchor points and ordinary data points in the layout simultaneously. Our approach presents much better and robust visual effects on 11 public datasets, and achieve 5 to 28 times speed-up on different datasets, compared with the current state-of-the-art methods. In particular, we deliver a high-quality 2-D layout for a 20 million and 96-dimension dataset within 5 hours, while the current methods fail to give results due to running out of the memory.

Subfields: Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lesia Semenova
2. Chudi Zhong
3. Zhi Chen
4. Haiyang Huang
5. Cynthia Rudin
6. David Chan
7. John F. Canny
8. Roshan Rao

In Computer Graphics and Computer-Aided Design

1. John F. Canny

In Computer Vision and Pattern Recognition

1. Cynthia Rudin
2. Zhi Chen
3. David Chan
4. Roshan Rao
5. Forrest Huang
6. Lesia Semenova
7. Chudi Zhong
8. Haiyang Huang

In Statistical and Nonlinear Physics

1. Lesia Semenova
2. John F. Canny
3. Chudi Zhong
4. Zhi Chen
5. Haiyang Huang
6. Cynthia Rudin

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “…the loss of global information by using global-aware initialization (McInnes et al., 2018; Kobak and Berens, 2019) (that is, initializing the distances in the low-dimensional space using PCA) and/or selectively preserving distances between non-neighbor samples (Fu et al., 2019; Wang et al., 2020b).”
• GPU accelerated t-distributed stochastic neighbor embedding (2019, methodology): “AtSNE: Anchor t-SNE [42] is a GPU accelerated t-SNE method which uses anchor points in the data to compute the repulsive forces.”