The traveling salesman problem (TSP) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. The TSP is probably the most famous and extensively studied problem in the field of combinatorial optimization. Because this problem is an NP-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

The traveling salesman problem (TSP) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. The TSP is probably the most famous and extensively studied problem in the field of combinatorial optimization. Because this problem is an NP-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

In this work, we propose a simple and effective scheme to incorporate prior knowledge about the sources of interest (SOIs) in independent component analysis (ICA) and apply the method to estimate brain activations from functional magnetic resonance imaging (fMRI) data. We name the proposed method as feature-selective ICA since it incorporates the features in the sample space of the independent ...

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address thi...

Discretization is an important preprocessing technique in data mining tasks. Univariate Discretization is the most commonly used method. It discretizes only one single attribute of a dataset at a time, without considering the interaction information with other attributes. Since it is multi-attribute rather than one single attribute determines the targeted class attribute, the result of Univaria...

Geometric algorithms for linear quadratic independent component analysis (ICA) have recently received some attention due to their pictorial description and their relative ease of implementation. The geometric approach to ICA has been proposed first by Puntonet and Prieto [1] [2] in order to separate linear mixtures. We generalize these algorithms to overcomplete cases with more sources than sen...

The learned parametric mixture method is presented for a canonical cost function based ICA model on linear mixture, with several new findings. First, its adaptive algorithm is further refined into a simple concise form. Second, the separation ability of this method is shown to be qualitatively superior to its original model with prefixed nonlinearity. Third, a heuristic way is suggested for sel...

The increasing requirement of classification categories is followed by the increasing probabilities of wrong classification and the decreasing classification speed. If we can separate certain types of pixels out in advance, and then classify the remaining pixels, we can reduce the probabilities of mistakes effectively. This paper proposed an improved Fast Independent Component Analysis (ICA) ba...

This paper presents a new algorithm for the independent components analysis (ICA) problem based on efficient entropy estimates. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with effi...

This paper addresses the blind source separation problem for the case where more sensors than source signals are available. A noisy-sensor model is assumed. The proposed algorithm comprises two stages, where the first stage consists of a principal component analysis (PCA) and the second one of an independent component analysis (ICA). The purpose of the PCA stage is to increase the input SNR of ...