Information Retrieval Perspective to Meta-visualization

Jaakko Peltonen, Ziyuan Lin
; Proceedings of the 5th Asian Conference on Machine Learning, PMLR 29:165-180, 2013.

Abstract

In visual data exploration with scatter plots, no single plot is sufficient to analyze complicated high-dimensional data sets. Given numerous visualizations created with different features or methods, meta-visualization is needed to analyze the visualizations together. We solve \emphhow to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. We introduce a machine learning approach to optimize the meta-visualization, based on an information retrieval perspective: two visualizations are similar if the analyst would retrieve similar neighborhoods between data samples from either visualization. Based on the approach, we introduce a nonlinear embedding method for meta-visualization: it optimizes locations of visualizations on a display, so that visualizations giving similar information about data are close to each other.

Cite this Paper


BibTeX
@InProceedings{pmlr-v29-Peltonen13, title = {Information Retrieval Perspective to Meta-visualization}, author = {Jaakko Peltonen and Ziyuan Lin}, booktitle = {Proceedings of the 5th Asian Conference on Machine Learning}, pages = {165--180}, year = {2013}, editor = {Cheng Soon Ong and Tu Bao Ho}, volume = {29}, series = {Proceedings of Machine Learning Research}, address = {Australian National University, Canberra, Australia}, month = {13--15 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v29/Peltonen13.pdf}, url = {http://proceedings.mlr.press/v29/Peltonen13.html}, abstract = {In visual data exploration with scatter plots, no single plot is sufficient to analyze complicated high-dimensional data sets. Given numerous visualizations created with different features or methods, meta-visualization is needed to analyze the visualizations together. We solve \emphhow to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. We introduce a machine learning approach to optimize the meta-visualization, based on an information retrieval perspective: two visualizations are similar if the analyst would retrieve similar neighborhoods between data samples from either visualization. Based on the approach, we introduce a nonlinear embedding method for meta-visualization: it optimizes locations of visualizations on a display, so that visualizations giving similar information about data are close to each other.} }
Endnote
%0 Conference Paper %T Information Retrieval Perspective to Meta-visualization %A Jaakko Peltonen %A Ziyuan Lin %B Proceedings of the 5th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Cheng Soon Ong %E Tu Bao Ho %F pmlr-v29-Peltonen13 %I PMLR %J Proceedings of Machine Learning Research %P 165--180 %U http://proceedings.mlr.press %V 29 %W PMLR %X In visual data exploration with scatter plots, no single plot is sufficient to analyze complicated high-dimensional data sets. Given numerous visualizations created with different features or methods, meta-visualization is needed to analyze the visualizations together. We solve \emphhow to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. We introduce a machine learning approach to optimize the meta-visualization, based on an information retrieval perspective: two visualizations are similar if the analyst would retrieve similar neighborhoods between data samples from either visualization. Based on the approach, we introduce a nonlinear embedding method for meta-visualization: it optimizes locations of visualizations on a display, so that visualizations giving similar information about data are close to each other.
RIS
TY - CPAPER TI - Information Retrieval Perspective to Meta-visualization AU - Jaakko Peltonen AU - Ziyuan Lin BT - Proceedings of the 5th Asian Conference on Machine Learning PY - 2013/10/21 DA - 2013/10/21 ED - Cheng Soon Ong ED - Tu Bao Ho ID - pmlr-v29-Peltonen13 PB - PMLR SP - 165 DP - PMLR EP - 180 L1 - http://proceedings.mlr.press/v29/Peltonen13.pdf UR - http://proceedings.mlr.press/v29/Peltonen13.html AB - In visual data exploration with scatter plots, no single plot is sufficient to analyze complicated high-dimensional data sets. Given numerous visualizations created with different features or methods, meta-visualization is needed to analyze the visualizations together. We solve \emphhow to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. We introduce a machine learning approach to optimize the meta-visualization, based on an information retrieval perspective: two visualizations are similar if the analyst would retrieve similar neighborhoods between data samples from either visualization. Based on the approach, we introduce a nonlinear embedding method for meta-visualization: it optimizes locations of visualizations on a display, so that visualizations giving similar information about data are close to each other. ER -
APA
Peltonen, J. & Lin, Z.. (2013). Information Retrieval Perspective to Meta-visualization. Proceedings of the 5th Asian Conference on Machine Learning, in PMLR 29:165-180

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