Abstract
One of the key advantages of visual analytics is its capability to leverage both humans's visual perception and the power of computing. A big obstacle in integrating machine learning with visual analytics is its high computing cost. To tackle this problem, this paper presents PIVE (Per-Iteration Visualization Environment) that supports real-time interactive visualization with machine learning. By immediately visualizing the intermediate results from algorithm iterations, PIVE enables users to quickly grasp insights and interact with the intermediate output, which then affects subsequent algorithm iterations. In addition, we propose a widely-applicable interaction methodology that allows efficient incorporation of user feedback into virtually any iterative computational method without introducing additional computational cost. We demonstrate the application of PIVE for various dimension reduction algorithms such as multidimensional scaling and t-SNE and clustering and topic modeling algorithms such as k-means and latent Dirichlet allocation.
| Original language | English |
|---|---|
| Pages | 1001-1009 |
| Number of pages | 9 |
| Publication status | Published - 2017 Jan 1 |
| Event | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: 2017 Feb 4 → 2017 Feb 10 |
Other
| Other | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
|---|---|
| Country | United States |
| City | San Francisco |
| Period | 17/2/4 → 17/2/10 |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
PIVE : Per-Iteration visualization environment for real-time interactions with dimension reduction and clustering. / Kim, Hannah; Choo, Jaegul; Lee, Changhyun; Lee, Hanseung; Reddy, Chandan K.; Park, Haesun.
2017. 1001-1009 Paper presented at 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States.Research output: Contribution to conference › Paper
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TY - CONF
T1 - PIVE
T2 - Per-Iteration visualization environment for real-time interactions with dimension reduction and clustering
AU - Kim, Hannah
AU - Choo, Jaegul
AU - Lee, Changhyun
AU - Lee, Hanseung
AU - Reddy, Chandan K.
AU - Park, Haesun
PY - 2017/1/1
Y1 - 2017/1/1
N2 - One of the key advantages of visual analytics is its capability to leverage both humans's visual perception and the power of computing. A big obstacle in integrating machine learning with visual analytics is its high computing cost. To tackle this problem, this paper presents PIVE (Per-Iteration Visualization Environment) that supports real-time interactive visualization with machine learning. By immediately visualizing the intermediate results from algorithm iterations, PIVE enables users to quickly grasp insights and interact with the intermediate output, which then affects subsequent algorithm iterations. In addition, we propose a widely-applicable interaction methodology that allows efficient incorporation of user feedback into virtually any iterative computational method without introducing additional computational cost. We demonstrate the application of PIVE for various dimension reduction algorithms such as multidimensional scaling and t-SNE and clustering and topic modeling algorithms such as k-means and latent Dirichlet allocation.
AB - One of the key advantages of visual analytics is its capability to leverage both humans's visual perception and the power of computing. A big obstacle in integrating machine learning with visual analytics is its high computing cost. To tackle this problem, this paper presents PIVE (Per-Iteration Visualization Environment) that supports real-time interactive visualization with machine learning. By immediately visualizing the intermediate results from algorithm iterations, PIVE enables users to quickly grasp insights and interact with the intermediate output, which then affects subsequent algorithm iterations. In addition, we propose a widely-applicable interaction methodology that allows efficient incorporation of user feedback into virtually any iterative computational method without introducing additional computational cost. We demonstrate the application of PIVE for various dimension reduction algorithms such as multidimensional scaling and t-SNE and clustering and topic modeling algorithms such as k-means and latent Dirichlet allocation.
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M3 - Paper
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SP - 1001
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