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Title: PIVE: Per-Iteration Visualization Environment for Real-Time Interactions with Dimension Reduction and Clustering
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.
Authors:
Award ID(s):
1707498 1646881 1619028
Publication Date:
NSF-PAR ID:
10048630
Journal Name:
Proceedings of the ... AAAI Conference on Artificial Intelligence
Page Range or eLocation-ID:
1001--1009
ISSN:
2374-3468
Sponsoring Org:
National Science Foundation
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