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Abstract: Eigenspaces are commonly used for dimensionality reduction, either for a compact encoding or for classification of high dimensional data. However in the presence of noise, the eigenspaces that were created using low-noise data, no longer explain the noise-corrupted data. In this paper, we present the notion of Noise Equivalent Dimensions (NED) as a means of increasing the contribution of signal strength to compensate for the contribution of noise. Although the notion of NED is created with... (Update)
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BibTeX entry: (Update)
@misc{ nc-noise,
author = "Rajeev Ramanath Nc",
title = "Noise Equivalent Dimensions in Eigenspaces",
url = "citeseer.ist.psu.edu/722217.html" }
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17 Robust recognition using eigenimages - Leonardis, Bischof - 2000
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