An E cient PCA-type Learning Based on Scaled Conjugate Gradient Algorithm for Fast Signal Subspace Decomposition
نویسندگان
چکیده
Nonlinear PCA type learning has been recently suggested for signal subspace decomposition and sinusoidal frequencies tracking, which outperformed the linear PCA based methods and traditional least squares algorithms. Currently, nonlinear PCA algorithms are directly generalized from linear ones that based on gradient descent (GD) technique. The convergence behavior of gradient descent is dependent upon the eigenvalue spread of the data correlation matrix and generally sensitive to the choice of the learning step size. In this paper, we proposed an e cient nonlinear PCA-type learning algorithm by using Scaled Conjugate Gradient (SCG) method proposed by Moller (1993) for signal subspace decomposition. SCG requires much less data samples and especially suitable for the large scale problems. The e ciency is demonstrated by simulations.
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