Semisupervised visualization of high-dimensional data
High-dimensional data visualization is a more complex process than the ordinary dimensionality reduction to two or three dimensions. Therefore, we propose and evaluate a novel four-step visualization approach that is built upon the combination of three components: metric learning, intrinsic dimensio...
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Veröffentlicht in: | Pattern recognition and image analysis 2007-12, Vol.17 (4), p.612-620 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | High-dimensional data visualization is a more complex process than the ordinary dimensionality reduction to two or three dimensions. Therefore, we propose and evaluate a novel four-step visualization approach that is built upon the combination of three components: metric learning, intrinsic dimensionality estimation, and feature extraction. Although many successful applications of dimensionality reduction techniques for visualization are known, we believe that the sophisticated nature of high-dimensional data often needs a combination of several machine learning methods to solve the task. Here, this is provided by a novel framework and experiments with real-world data.[PUBLICATION ABSTRACT] |
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ISSN: | 1054-6618 1555-6212 |
DOI: | 10.1134/S1054661807040220 |