Multi-Channel Subspace Mapping Using an Information Maximization Criterion
Multidimensional Systems and Signal Processing
Blind multi-user-detection for TH-UWB systems in UWB channels
ICCOM'08 Proceedings of the 12th WSEAS international conference on Communications
Fast subspace tracking algorithm based on the constrained projection approximation
EURASIP Journal on Advances in Signal Processing
Low complexity DFT-domain noise PSD tracking using high-resolution periodograms
EURASIP Journal on Advances in Signal Processing
Fast training of neural networks for image compression
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Hi-index | 35.68 |
We introduce a novel information criterion (NIC) for searching for the optimum weights of a two-layer linear neural network (NN). The NIC exhibits a single global maximum attained if and only if the weights span the (desired) principal subspace of a covariance matrix. The other stationary points of the NIC are (unstable) saddle points. We develop an adaptive algorithm based on the NIC for estimating and tracking the principal subspace of a vector sequence. The NIC algorithm provides a fast on-line learning of the optimum weights for the two-layer linear NN. We establish the connections between the NIC algorithm and the conventional mean-square-error (MSE) based algorithms such as Oja's algorithm (Oja 1989), LMSER, PAST, APEX, and GHA. The NIC algorithm has several key advantages such as faster convergence, which is illustrated through analysis and simulation