DocumentCode
394401
Title
Unsupervised classification by spectral ICA
Author
Szu, Harold
Author_Institution
Office of Naval Res., Arlington, VA, USA
Volume
4
fYear
2002
fDate
18-22 Nov. 2002
Firstpage
1760
Abstract
Unsupervised classification is defined such that the information required to do so must be learned and derived directly and solely from the data alone, this is consistent with the classical definition "unlabelled data" ATR by Duda and Hart. Such a truly unsupervised methodology is presented for space-variant imaging for breast cancer detection by means of a spectral-ICA methodology rather than by spatial-ICA for space-invariant imaging.
Keywords
biomedical optical imaging; image classification; independent component analysis; infrared imaging; mammography; medical image processing; unsupervised learning; breast cancer detection; hyperspectral sensors; independent component analysis; infrared radiation; neural nets; space-variant imaging; spectral ICA; thermal breast scanning; unsupervised classification; Breast cancer; Cancer detection; Independent component analysis; Lagrangian functions; Optical imaging; Physics; Pixel; Remote sensing; Surveillance; Thermodynamics;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN
981-04-7524-1
Type
conf
DOI
10.1109/ICONIP.2002.1198976
Filename
1198976
Link To Document