Applications of Neural Blind Separation to Signal and Image Processing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Face Recognition Based on WT, FastICA and RBF Neural Network
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Face Recognition Based on FastICA and RBF Neural Networks
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 01
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
Implementation of Pipelined FastICA on FPGA for Real-Time Blind Source Separation
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper, an improved algorithm based on fast ICA and optimum selection for IR objects recognition is proposed. Directed against the problem that the Newton iteration is rather sensitive to the selection of initial value, this paper presents a one dimension search to improve its optimum learning algorithm in order to make the convergence of the results independent of the selection of the initial value. Meanwhile, we design a novel rule for the distance function to retain the features of the independent component having major contribution to object recognition. It overcomes the problem of declining of recognition rate and robustness associated with the increasing of training image samples. Compared with traditional methods the proposed algorithm can reach a higher recognition rate with fewer IR objects features and is more robust in different kinds of classes.