Schreck, Tobias ; Schneidewind, Jörn ; Keim, Daniel (2008)
An Image-Based Approach to Visual Feature Space Analysis.
WSCG 2008. Communications Papers.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Methods for management and analysis of non-standard data often rely on the so-called feature vector approach. The technique describes complex data instances by vectors of characteristic numeric values which allow to index the data and to calculate similarity scores between the data elements. Thereby, feature vectors often are a key ingredient to intelligent data analysis algorithms including instances of clustering, classification, and similarity search algorithms. However, identification of appropriate feature vectors for a given database of a given data type is a challenging task. Determining good feature vector extractors usually involves benchmarks relying on supervised information, which makes it an expensive and data dependent process. In this paper, we address the feature selection problem by a novel approach based on analysis of certain feature space images. We develop two image-based analysis techniques for the automatic discrimination power analysis of feature spaces. We evaluate the techniques on a comprehensive feature selection benchmark, demonstrating the effectiveness of our analysis and its potential toward automatically addressing the feature selection problem.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2008 |
| Autor(en): | Schreck, Tobias ; Schneidewind, Jörn ; Keim, Daniel |
| Art des Eintrags: | Bibliographie |
| Titel: | An Image-Based Approach to Visual Feature Space Analysis |
| Sprache: | Deutsch |
| Publikationsjahr: | 2008 |
| Verlag: | University of West Bohemia, Plzen |
| Veranstaltungstitel: | WSCG 2008. Communications Papers |
| Kurzbeschreibung (Abstract): | Methods for management and analysis of non-standard data often rely on the so-called feature vector approach. The technique describes complex data instances by vectors of characteristic numeric values which allow to index the data and to calculate similarity scores between the data elements. Thereby, feature vectors often are a key ingredient to intelligent data analysis algorithms including instances of clustering, classification, and similarity search algorithms. However, identification of appropriate feature vectors for a given database of a given data type is a challenging task. Determining good feature vector extractors usually involves benchmarks relying on supervised information, which makes it an expensive and data dependent process. In this paper, we address the feature selection problem by a novel approach based on analysis of certain feature space images. We develop two image-based analysis techniques for the automatic discrimination power analysis of feature spaces. We evaluate the techniques on a comprehensive feature selection benchmark, demonstrating the effectiveness of our analysis and its potential toward automatically addressing the feature selection problem. |
| Freie Schlagworte: | Forschungsgruppe Visual Search and Analysis (VISA), Visual analytics, Automatic feature selection, Self-organizing maps (SOM) |
| Fachbereich(e)/-gebiet(e): | nicht bekannt 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Apr 2018 09:03 |
| Letzte Änderung: | 16 Apr 2018 09:03 |
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