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This paper discusses the source recovery step in two-stage blind separation algorithm of underdetermined mixtures. A statistically non-sparse decomposition principle of two mixtures (2d-SNSDP), which is an extension of the SSDP algorithm about two mixtures, is proposed. It overcomes the disadvantage of the SSDP algorithm and sparse representation based on l1-norm. Compared with traditional sparse...
Feature selection is the technique commonly used in machine learning to select a subset of relevant features for building robust learning models. Ensemble feature relevance determination can properly group the most relevant features together and separate the relevant features from the irrelevant and redundant features. However, it cannot provide reliable local feature relevance rank. In this paper,...
An integrated approach based on innovation diffusion theory and lifestyle theory for customer segmentation of mobile commerce on the train using multivariate statistical analysis is proposed for Taiwan Railway Administration. Firstly, the contents of mobile commerce on the train are identified as segmentation variables and key factor facets for mobile commerce are redefined by using factor analysis...
To implement visual target classification, this paper proposes a collaborative statistical learning algorithm for online support vector machine(SVM) classifier learning in wireless multimedia sensor network (WMSN). For achieving robust target classification, classifier learning should be carried out iteratively for updating classifiers according to various situations. Because only unlabeled samples...
In this paper, a new line symmetry based classifier (LSC) is proposed to deal with pattern classification problems. In order to measure total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also proposed in this paper. The proposed line symmetry based classifier (LSC) utilizes this new definition of line symmetry distance for classifying...
In this paper the use of the neural network emulation technique, developed earlier by the authors, is investigated in application to ensembles of general circulation models used for the weather prediction and climate simulation. It is shown that the neural network emulation technique allows us: (1) to introduce fast versions of model physics (or components of model physics) that can speed up calculations...
High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the others. In this paper, we propose a new clustering algorithm to solve these problems. The basic idea is to control the support of the optimization procedure so that the effect produced by those contaminated samples and dimensions is greatly...
This paper proposes a novel classification method for image retrieval using gradient-based fuzzy c-means with divergence measure (GBFCM(DM)). GBFCM(DM) is a neural network-based algorithm that utilizes the Divergence Measure to exploit the statistical nature of the image data and thereby improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed...
Listening to music, as per clinical neuro science, involves many cognitive components with distinct brain substrates and its study has advanced greatly in the last three decades. But the studies of Indian music and its influence in the brain have not yet been studied. This article presents sequence of image processing steps using statistical parametric mapping for the analysis of fMRI brain structures...
This study proposes an analytic approach that combines LISREL and Bayesian networks (BN) to examine factors influencing tourism loyalty and predict a touristpsilas loyalty level. LISREL is used to verify the hypothesized relationships proposed in the research model. Subsequently, the supported relationships are used as the BN network structure for prediction. 452 valid samples were collected from...
This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used...
The KIV model is a biologically inspired hierarchical model that describes non-linear dynamics found in brains. Previous animal and human EEG measurements indicated the presence of jumps in the spatio-temporal EEG patterns, which are relevant to cognitive processing. The present work introduces the KIV model to simulate phase transitions in EEG signals. Phase transitions have non-stationary and intermittent...
Ventilator weaning is the process of discontinuing mechanical ventilation from patients with respiratory failure. Ventilator support should be withdrawn as soon as possible when it is no longer necessary in order to reduce the likelihood of known nosocomial complications and costs. Previous investigation indicated that clinicians were often wrong when predicting weaning outcome. The motivation of...
We characterize three small gene signatures derived consequently from the original 232-gene breast cancer aggressiveness signature which could improve biological classification and clinical assignment of ~50% of breast cancer patients having histologic grade 2 tumors . Here, we develop a novel approach to identify small gene signatures providing statistically reliable, biological important and clinical...
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