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IJCNN
2000
IEEE
15 years 2 months ago
ICA for Noisy Neurobiological Data
ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the field of neurobiological data analysis such as EEG (...
Shiro Ikeda, Keisuke Toyama
WSCG
2004
166views more  WSCG 2004»
14 years 11 months ago
De-noising and Recovering Images Based on Kernel PCA Theory
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...
Pengcheng Xi, Tao Xu
ICANN
2001
Springer
15 years 2 months ago
Feature Extraction Using ICA
In manipulating data such as in supervised learning, we often extract new features from original features for the purpose of reducing the dimensions of feature space and achieving ...
Nojun Kwak, Chong-Ho Choi, Jin-Young Choi
CLOR
2006
15 years 1 months ago
Components for Object Detection and Identification
We present a component-based system for object detection and identification. From a set of training images of a given object we extract a large number of components which are clust...
Bernd Heisele, Ivaylo Riskov, Christian Morgenster...
MICCAI
2003
Springer
15 years 10 months ago
Exploratory Identification of Cardiac Noise in fMRI Images
A fast exploratory framework for extracting cardiac noise signals contained in rest-case fMRI images is presented. Highly autocorrelated, independent components of the input time ...
Lilla Zöllei, Lawrence P. Panych, W. Eric L. ...