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Information retrieval approach to meta-visualization

Tutkimustuotosvertaisarvioitu

Standard

Information retrieval approach to meta-visualization. / Peltonen, Jaakko; Lin, Ziyuan.

julkaisussa: Machine Learning, Vuosikerta 99, Nro 2, 01.05.2015, s. 189-229.

Tutkimustuotosvertaisarvioitu

Harvard

Peltonen, J & Lin, Z 2015, 'Information retrieval approach to meta-visualization', Machine Learning, Vuosikerta. 99, Nro 2, Sivut 189-229. https://doi.org/10.1007/s10994-014-5464-x

APA

Peltonen, J., & Lin, Z. (2015). Information retrieval approach to meta-visualization. Machine Learning, 99(2), 189-229. https://doi.org/10.1007/s10994-014-5464-x

Vancouver

Peltonen J, Lin Z. Information retrieval approach to meta-visualization. Machine Learning. 2015 touko 1;99(2):189-229. https://doi.org/10.1007/s10994-014-5464-x

Author

Peltonen, Jaakko ; Lin, Ziyuan. / Information retrieval approach to meta-visualization. Julkaisussa: Machine Learning. 2015 ; Vuosikerta 99, Nro 2. Sivut 189-229.

Bibtex - Lataa

@article{5cb35d5f2f8d44728de5af6d9bbc141a,
title = "Information retrieval approach to meta-visualization",
abstract = "Visualization is crucial in the first steps of data analysis. 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 how to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. Visualization has recently been formalized as an information retrieval task; we extend this approach, and formalize meta-visualization as an information retrieval task whose performance can be rigorously quantified and optimized. 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. In experiments we show such meta-visualization outperforms alternatives, and yields insight into data in several case studies.",
keywords = "Meta-visualization, Neighbor embedding, Nonlinear dimensionality reduction",
author = "Jaakko Peltonen and Ziyuan Lin",
year = "2015",
month = "5",
day = "1",
doi = "10.1007/s10994-014-5464-x",
language = "English",
volume = "99",
pages = "189--229",
journal = "Machine Learning",
issn = "0885-6125",
publisher = "Springer Verlag",
number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Information retrieval approach to meta-visualization

AU - Peltonen, Jaakko

AU - Lin, Ziyuan

PY - 2015/5/1

Y1 - 2015/5/1

N2 - Visualization is crucial in the first steps of data analysis. 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 how to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. Visualization has recently been formalized as an information retrieval task; we extend this approach, and formalize meta-visualization as an information retrieval task whose performance can be rigorously quantified and optimized. 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. In experiments we show such meta-visualization outperforms alternatives, and yields insight into data in several case studies.

AB - Visualization is crucial in the first steps of data analysis. 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 how to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. Visualization has recently been formalized as an information retrieval task; we extend this approach, and formalize meta-visualization as an information retrieval task whose performance can be rigorously quantified and optimized. 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. In experiments we show such meta-visualization outperforms alternatives, and yields insight into data in several case studies.

KW - Meta-visualization

KW - Neighbor embedding

KW - Nonlinear dimensionality reduction

UR - http://www.scopus.com/inward/record.url?scp=84939887799&partnerID=8YFLogxK

U2 - 10.1007/s10994-014-5464-x

DO - 10.1007/s10994-014-5464-x

M3 - Article

VL - 99

SP - 189

EP - 229

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 2

ER -