Non-linear Mapping for Feature Extraction
نویسندگان
چکیده
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 di erent non-linear 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 classi cation tasks. The experiments involve an arti cial data set and grey-level and color texture data sets.
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