Independent component analysis （ICA） is the primary statistical method for solving the problems of blind source separation. The fast ICA is a famous and excellent algorithm and its contrast function is optimized by ...Independent component analysis （ICA） is the primary statistical method for solving the problems of blind source separation. The fast ICA is a famous and excellent algorithm and its contrast function is optimized by the quadratic convergence of Newton iteration method. In order to improve the convergence speed and the separation precision of the fast ICA, an improved fast ICA algorithm is presented. The algorithm introduces an efficient Newton＇s iterative method with fifth-order convergence for optimizing the contrast function and gives the detail derivation process and the corresponding condition. The experimental results demonstrate that the convergence speed and the separation precision of the improved algorithm are better than that of the fast ICA.展开更多
One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is propo...One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection.展开更多
文摘Independent component analysis （ICA） is the primary statistical method for solving the problems of blind source separation. The fast ICA is a famous and excellent algorithm and its contrast function is optimized by the quadratic convergence of Newton iteration method. In order to improve the convergence speed and the separation precision of the fast ICA, an improved fast ICA algorithm is presented. The algorithm introduces an efficient Newton＇s iterative method with fifth-order convergence for optimizing the contrast function and gives the detail derivation process and the corresponding condition. The experimental results demonstrate that the convergence speed and the separation precision of the improved algorithm are better than that of the fast ICA.
基金Supported by the 973 Project (No.2003CB716106), NSFC (No.90208003, 30200059), TRAPOYT, Doctor Training Fund of MOE, PRC, Key Research Project of Science and Technology of MOE, Fok Ying Tong Education Foundation (No.91041)
文摘One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection.
