학술논문
Determining hyperspectral data-intrinsic dimensionality via a modified Gram-Schmidt process
Document Type
Conference
Author
Source
2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel Electrical and electronics engineers in Israel Electrical and Electronics Engineers in Israel, 2004. Proceedings. 2004 23rd IEEE Convention of. :380-383 2004
Subject
Language
Abstract
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.