Name
Abstract | ||
|---|---|---|
The growing number of dimensionality reduction methods available for data visualization has recently inspired the development of formal measures to evaluate the resulting low-dimensional representation independently from the methods' inherent criteria. Many evaluation measures can be summarized based on the co-ranking matrix. In this work, we analyze the characteristics of the co-ranking framework, focusing on interpretability and controllability in evaluation scenarios where a fine-grained assessment of a given visualization is desired. We extend the framework in two ways: (i) we propose how to link the evaluation to point-wise quality measures which can be used directly to augment the evaluated visualization and highlight erroneous regions; (ii) we improve the parameterization of the quality measure to offer more direct control over the evaluation's focus, and thus help the user to investigate more specific characteristics of the visualization. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2012.11.046 | the european symposium on artificial neural networks |
Keywords | DocType | Volume |
evaluation measure,direct control,dimensionality reduction method,erroneous region,co-ranking framework,quality measure,evaluation scenario,co-ranking matrix,data visualization,fine-grained assessment,nonlinear dimensionality reduction | Journal | 112 |
ISSN | Citations | PageRank |
0925-2312 | 26 | 0.75 |
References | Authors | |
23 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bassam Mokbel | 1 | 189 | 14.73 |
| Wouter Lueks | 2 | 102 | 8.12 |
| Andrej Gisbrecht | 3 | 195 | 15.60 |
| Michael Biehl | 4 | 784 | 62.50 |
| Barbara Hammer | 5 | 2383 | 181.34 |