Abstract
Mapping techniques have been regularly used for visualization of high-dimensional data sets. In this paper, mapping to d ≥ 2 is studied, with the purpose of feature extraction. Two different nonlinear techniques are studied: self-organizing maps and auto-associative feedforward networks. The non-linear techniques are compared to linear Principal Component Analysis (PCA). A comparison with respect to feature extraction is made by evaluating the reduced feature sets ability to perform classification tasks. The experiments involve an artificial data set and grey-level and color texture data sets.
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Keywords
- Feature Extraction
- Classification Performance
- Output Space
- Feature Selection Technique
- Feature Extraction Technique
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Scheunders, P., De Backer, S., Naud, A. (1998). Non-linear mapping for feature extraction. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033307
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DOI: https://doi.org/10.1007/BFb0033307
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