Métrica Induzida da Correntropia Complexa Comparada ao NESTA no Problema de Amostragem Compressiva / Induced Complex Correntropy Metric Compared to NESTA on the Compressive Sampling Problem

Esse artigo compara ao algoritmo de Nesterov (NESTA) o desempenho da métrica induzida da correntropia complexa (Complex Correntropy Induced Metric - CCIM) enquanto uma aproximação de l0 num problema de amostragem compressiva. As simulações mostram que a CCIM é capaz de reconstruir um vetor esparso complexo usando menos medidas do que o NEST

Generalized correntropy induced metric based total least squares for sparse system identification

The total least squares (TLS) method has been successfully applied to system identification in the errors-in-variables (EIV) model, which can efficiently describe systems where input–output pairs are contaminated by noise. In this paper, we propose a new gradient-descent TLS filtering algorithm based on the generalized correntropy induced metric (GCIM), called as GCIM-TLS, for sparse system identification. By introducing GCIM as a penalty term to the TLS problem, we can achieve improved accuracy of sparse system identification. We also characterize the convergence behaviour analytically for GCIM-TLS. To reduce computational complexity, we use the first-order Taylor series expansion and further derive a simplified version of GCIM-TLS. Simulation results verify the effectiveness of our proposed algorithms in sparse system identification

Quantized mixture kernel least mean square

Abstract—Use of multiple kernels in the conventional kernel algorithms is gaining much popularity as it addresses the kernel selection problem as well as improves the performance. Kernel least mean square (KLMS) has been extended to multiple kernels recently using different approaches, one of which is mixture kernel least mean square (MxKLMS). Although this method addresses the kernel selection problem, and improves the performance, it suffers from a problem of linearly growing dictionary like in KLMS. In this paper, we present the quantized MxKLMS (QMxKLMS) algorithm to achieve sub-linear growth in dictionary. This method quantizes the input space based on the conventional criteria using Euclidean distance in input space as well as a new criteria using Euclidean distance in RKHS induced by the sum kernel. The empirical results suggest that QMxKLMS using the latter metric is suitable in a non-stationary environment with abruptly changing modes as they are able to utilize the information regarding the relative importance of ker-nels. Moreover, the QMxKLMS using both metrics are compared with the QKLMS and the existing multi-kernel methods MKLMS and MKNLMS-CS, showing an improved performance over these methods. I

Novel Deep Learning Techniques For Computer Vision and Structure Health Monitoring

This thesis proposes novel techniques in building a generic framework for both the regression and classification tasks in vastly different applications domains such as computer vision and civil engineering. Many frameworks have been proposed and combined into a complex deep network design to provide a complete solution to a wide variety of problems. The experiment results demonstrate significant improvements of all the proposed techniques towards accuracy and efficiency

A Novel Nonparametric Distance Estimator for Densities with Error Bounds

The use of a metric to assess distance between probability densities is an important practical problem. In this work, a particular metric induced by an a-divergence is studied. The Hellinger metric can be interpreted as a particular case within the framework of generalized Tsallis divergences and entropies. The nonparametric Parzen's density estimator emerges as a natural candidate to estimate the underlying probability density function, since it may account for data from different groups, or experiments with distinct instrumental precisions, i.e., non-independent and identically distributed (non-i.i.d.) data. However, the information theoretic derived metric of the nonparametric Parzen's density estimator displays infinite variance, limiting the direct use of resampling estimators. Based on measure theory, we present a change of measure to build a finite variance density allowing the use of resampling estimators. In order to counteract the poor scaling with dimension, we propose a new nonparametric two-stage robust resampling estimator of Hellinger's metric error bounds for heterocedastic data. The approach presents very promising results allowing the use of different covariances for different clusters with impact on the distance evaluation