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Determining hyperspectral data-intrinsic dimensionality via a modified Gram-Schmidt process

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3 Author(s)
Kuybeda, O. ; Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel ; Kagan, A. ; Lumer, Y.

The overdetermined nature of hyperspectral data constitutes a serious obstacle in many applicative fields. A vital step in dimensionality reduction is determining the intrinsic number of dimensions the signal resides in. This work proposes a modified Gram-Schmidt (MGS) process which iteratively finds the most distant pixels within the data in terms of an orthogonal complement norm (OCN) to a subspace spanned by the extreme pixels found in previous iterations. We analyze the distribution of extreme OCN using extreme values theory (EVT) and derive a termination condition for the MGS process. The dimensionality is determined by the number of found extreme pixels, which provide an estimation for the signal subspace.

Published in:

Electrical and Electronics Engineers in Israel, 2004. Proceedings. 2004 23rd IEEE Convention of

Date of Conference:

6-7 Sept. 2004