Information retrieval approach to meta-visualization
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605478562 | ||
| 003 | CHVBK | ||
| 005 | 20210128100406.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150501xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-014-5464-x |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5464-x | ||
| 245 | 0 | 0 | |a Information retrieval approach to meta-visualization |h [Elektronische Daten] |c [Jaakko Peltonen, Ziyuan Lin] |
| 520 | 3 | |a 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. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Meta-visualization |2 nationallicence | |
| 690 | 7 | |a Neighbor embedding |2 nationallicence | |
| 690 | 7 | |a Nonlinear dimensionality reduction |2 nationallicence | |
| 700 | 1 | |a Peltonen |D Jaakko |u Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076, Aalto, Finland |4 aut | |
| 700 | 1 | |a Lin |D Ziyuan |u Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076, Aalto, Finland |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 99/2(2015-05-01), 189-229 |x 0885-6125 |q 99:2<189 |1 2015 |2 99 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5464-x |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5464-x |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Peltonen |D Jaakko |u Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076, Aalto, Finland |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Lin |D Ziyuan |u Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076, Aalto, Finland |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 99/2(2015-05-01), 189-229 |x 0885-6125 |q 99:2<189 |1 2015 |2 99 |o 10994 | ||