Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are l2 distance or KullbackLeibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels,...

<span lang="EN-US">The next generation of wireless cellular communication networks must be energy efficient, extremely reliable, and have low latency, leading to the necessity using algorithms based on deep neural (DNN) which better bit error rate (BER) or symbol (SER) performance than traditional complex multi-antenna multi-input multi-output (MIMO) detectors. This paper examines iterati...

Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract...

In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we pro...

Abstract Most of the cost functions adaptive filtering algorithms include square error, which depends on current error signal. When additive noise is impulsive, we can expect that will be very large. By contrast, cross correlation signal and its delay, may small. Based this fact, propose a new function called mean for filters, provide value performance analysis in detail. Furthermore, present t...

In this paper, the development and performance study of information-theoretic learning (ITL) based blind equalizer algorithms for inter-symbol interference communication channel environments with a mixture of white Gaussian noise and impulsive noise are presented. A new developed blind equalizer algorithm based on matching two probability density functions of outputs and desired symbols employs...

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the l2 distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of ...

Article history: Received 25 June 2008 Received in revised form 22 September 2008 Accepted 23 September 2008