Frontiers in Signal Processing

16

Authors: Zhao Dongbo, Li Hui

Least squares support vector machine (LS-SVM) has a large amount of computation and sparsity. Aiming at this problem, a fast sparse approximation least squares support vector machine (FSALS-SVM) algorithm is proposed. The algorithm uses an iterative algorithm of complexity to ac-celerate the calculation of the inverse of the kernel matrix and the sparse processing of the support vector machine through the pruning algorithm, thus reducing the computational complexity. The classification and recognition experiments of one dimension range profile of one dimensional radar target show that the FSALS-SVM is more sparsely under the same generalization performance.

29

Authors: Yuriy S. Shmaliy, Yrjö Neuvo, Sanowar Khan

Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided.