Active Learning for Interactive Visualization

Tomoharu Iwata, Neil Houlsby, Zoubin Ghahramani
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:342-350, 2013.

Abstract

Many automatic visualization methods have been proposed. However, a visualization that is automatically generated might be different to how a user wants to arrange the objects in visualization space. By allowing users to re-locate objects in the embedding space of the visualization, they can adjust the visualization to their preference. We propose an active learning framework for interactive visualization which selects objects for the user to re-locate so that they can obtain their desired visualization by re-locating as few as possible. The framework is based on an information theoretic criterion, which favors objects that reduce the uncertainty of the visualization. We present a concrete application of the proposed framework to the Laplacian eigenmap visualization method. We demonstrate experimentally that the proposed framework yields the desired visualization with fewer user interactions than existing methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v31-iwata13a, title = {Active Learning for Interactive Visualization}, author = {Iwata, Tomoharu and Houlsby, Neil and Ghahramani, Zoubin}, booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, pages = {342--350}, year = {2013}, editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, volume = {31}, series = {Proceedings of Machine Learning Research}, address = {Scottsdale, Arizona, USA}, month = {29 Apr--01 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v31/iwata13a.pdf}, url = {https://proceedings.mlr.press/v31/iwata13a.html}, abstract = {Many automatic visualization methods have been proposed. However, a visualization that is automatically generated might be different to how a user wants to arrange the objects in visualization space. By allowing users to re-locate objects in the embedding space of the visualization, they can adjust the visualization to their preference. We propose an active learning framework for interactive visualization which selects objects for the user to re-locate so that they can obtain their desired visualization by re-locating as few as possible. The framework is based on an information theoretic criterion, which favors objects that reduce the uncertainty of the visualization. We present a concrete application of the proposed framework to the Laplacian eigenmap visualization method. We demonstrate experimentally that the proposed framework yields the desired visualization with fewer user interactions than existing methods.} }
Endnote
%0 Conference Paper %T Active Learning for Interactive Visualization %A Tomoharu Iwata %A Neil Houlsby %A Zoubin Ghahramani %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep Ravikumar %F pmlr-v31-iwata13a %I PMLR %P 342--350 %U https://proceedings.mlr.press/v31/iwata13a.html %V 31 %X Many automatic visualization methods have been proposed. However, a visualization that is automatically generated might be different to how a user wants to arrange the objects in visualization space. By allowing users to re-locate objects in the embedding space of the visualization, they can adjust the visualization to their preference. We propose an active learning framework for interactive visualization which selects objects for the user to re-locate so that they can obtain their desired visualization by re-locating as few as possible. The framework is based on an information theoretic criterion, which favors objects that reduce the uncertainty of the visualization. We present a concrete application of the proposed framework to the Laplacian eigenmap visualization method. We demonstrate experimentally that the proposed framework yields the desired visualization with fewer user interactions than existing methods.
RIS
TY - CPAPER TI - Active Learning for Interactive Visualization AU - Tomoharu Iwata AU - Neil Houlsby AU - Zoubin Ghahramani BT - Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics DA - 2013/04/29 ED - Carlos M. Carvalho ED - Pradeep Ravikumar ID - pmlr-v31-iwata13a PB - PMLR DP - Proceedings of Machine Learning Research VL - 31 SP - 342 EP - 350 L1 - http://proceedings.mlr.press/v31/iwata13a.pdf UR - https://proceedings.mlr.press/v31/iwata13a.html AB - Many automatic visualization methods have been proposed. However, a visualization that is automatically generated might be different to how a user wants to arrange the objects in visualization space. By allowing users to re-locate objects in the embedding space of the visualization, they can adjust the visualization to their preference. We propose an active learning framework for interactive visualization which selects objects for the user to re-locate so that they can obtain their desired visualization by re-locating as few as possible. The framework is based on an information theoretic criterion, which favors objects that reduce the uncertainty of the visualization. We present a concrete application of the proposed framework to the Laplacian eigenmap visualization method. We demonstrate experimentally that the proposed framework yields the desired visualization with fewer user interactions than existing methods. ER -
APA
Iwata, T., Houlsby, N. & Ghahramani, Z.. (2013). Active Learning for Interactive Visualization. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 31:342-350 Available from https://proceedings.mlr.press/v31/iwata13a.html.

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