Approach and applications of constrained ICA.
This paper presents the technique of constrained independent component analysis (cICA) and demonstrates two applications, less-complete ICA, and ICA with reference (ICA-R). The cICA is proposed as a general framework to incorporate additional requirements and prior information in the form of constraints into the ICA contrast function. The adaptive solutions using the Newton-like learning are proposed to solve the constrained optimization problem. The applications illustrate the versatility of the cICA by separating subspaces of independent components according to density types and extracting a set of desired sources when rough templates are available. The experiments using face images and functional MR images demonstrate the usage and efficacy of the cICA.
Lu
Wei
W
School of Computer Engineering, Nanyang Technological University, Singapore 639798. wei.lu@ap.sony.com
Rajapakse
Jagath C
JC
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D003205:Computing Methodologies; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods; D025341:Principal Component Analysis