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This paper forms a part of a series of recent studies we have undertaken, where the problem of nonlinear signal modelling is examined. We assume that an observed "output" signal is derived from a Volterra filter that is driven by a Gaussian input. Both the filter parameters and the input signal are unknown and therefore the problem can be classified as blind or unsupervised in nature. In...
System Identification has been developed, by and large, following the classical parametric approach. In this tutorial we shall discuss how Bayesian statistics and regularization theory can be employed to tackle the system identification problem from a nonparametric (or semi-parametric) point of view. The present paper provides an introduction to the use of Bayesian techniques for smoothness and sparseness,...
To improve efficiency of power amplifier (PA), linearity characteristics is often compromised when targeting lower power consumption (class B). Moreover, sophisticated PA efficiency improvement schemes such as envelope tracking tend to further boost the nonlinear characteristics of the PA. Digital pre-distortion (DPD) is a technique to improve the linearity of a power amplifier (PA) at expense of...
Multikernel learning (MKL) has recently received great attention in the field of computer vision and pattern recognition. The idea behind MKL is to optimally combine and utilize multiple kernels and features instead of using a single kernel in learning classifiers. This paper presents a novel framework for MKL problem by expanding the HessianMKL algorithm into multiclass-SVM with one-against-one rule...
Spectral clustering makes use of spectral-graph structure of an affinity matrix to partition data into disjoint meaningful groups. Because of its elegance, efficiency and good performance, spectral clustering has become one of the most popular clustering methods. Traditional spectral clustering assumes a single affinity matrix. However, in many applications, there could be multiple potentially useful...
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