Visualizing the quality of dimensionality reduction
| Autor: | Wouter Lueks, Andrej Gisbrecht, Barbara Hammer, Bassam Mokbel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Nonlinear dimensionality reduction
Computer science assessment Cognitive Neuroscience media_common.quotation_subject computer.software_genre Machine learning Data visualization Artificial Intelligence Quality (business) Representation (mathematics) Co-ranking matrix media_common Interpretability PROJECTION business.industry Dimensionality reduction FRAMEWORK Quality Computer Science Applications Visualization Data mining Artificial intelligence Digital Security business computer Quality assessment |
| Zdroj: | Neurocomputing, 112, July, pp. 109-123 Neurocomputing, 112, 109-123 Neurocomputing, 112, 109-123. ELSEVIER SCIENCE BV |
| ISSN: | 0925-2312 |
| Popis: | 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. (C) 2013 Elsevier B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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