Differently from Vector-pattern-oriented Classifier Design (VecCD), Matrix-pattern-oriented Classifier Design (MatCD) is expected to manipulate matrix-oriented patterns directly rather than turning them into a vector,...
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Differently from Vector-pattern-oriented Classifier Design (VecCD), Matrix-pattern-oriented Classifier Design (MatCD) is expected to manipulate matrix-oriented patterns directly rather than turning them into a vector, and further demonstrated its effectiveness. however, some prior information, such as the local sensitive discriminant information among matrix-oriented patterns, might be neglected by MatCD. To overcome such flaw, a new regularization term named Rim is adopted into MatCD by taking advantage of Locality Sensitive Discriminant Analysis (LSDA) in this paper. In detail, the objective function of LSDA is modified and transformed into the regularization term RED to explore the local sensitive discriminant information among matrix-oriented patterns. In the implementation, R-LSD is collaborated with one typical MatCD, whose name is Matrix-pattern-oriented ho-kashyap Classifier (MatMHKS), so as to create a new classifier based on local sensitive discriminant information named LSDMatMHKS for short. Finally, comprehensive experiments are designed to validate the effectiveness of LSDMatMHKS. The major contributions of this paper can be concluded as (1) improving the classification performance and the learning ability of MatCD, (2) introducing local sensitive discriminant information into MatCD and extending the application scenario of LSDA, and (3) validating and analyzing the feasibility and effectiveness of R-LSD). (C) 2016 Elsevier B.V. All rights reserved.
Inspired by the matrix-based methods used in feature extraction and selection, one matrix-pattern-oriented classification framework has been designed in our previous work and demonstrated to utilize one matrix pattern...
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Inspired by the matrix-based methods used in feature extraction and selection, one matrix-pattern-oriented classification framework has been designed in our previous work and demonstrated to utilize one matrix pattern itself more effectively to improve the classification performance in practice. however, this matrix-based framework neglects the prior structural information of the whole input space that is made up of all the matrix patterns. This paper aims to overcome such flaw through taking advantage of one structure learning method named Alternative Robust Local Embedding (ARLE). As a result, a new regularization term R-gl is designed, expected to simultaneously represent the globality and the locality of the whole data domain, further boosting the existing matrix-based classification method. To our knowledge, it is the first trial to introduce both the globality and the locality of the whole data space into the matrixized classifier design. In order to validate the proposed approach, the designed Rgi is applied into the previous work matrix-pattern-oriented ho-kashyap classifier (MatMHKS) to construct a new globalized and localized MatMHKS named GLMatMHKS. The experimental results on a broad range of data validate that GLMatMHKS not only inherits the advantages of the matrixized learning, but also uses the prior structural information more reasonably to guide the classification machine design. (C) 2014 Elsevier B.V. All rights reserved.
In the framework of remote-sensing image classification, support vector machines (SVMs) have lately been receiving substantial attention due to their accurate results in many applications as well as their remarkable g...
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In the framework of remote-sensing image classification, support vector machines (SVMs) have lately been receiving substantial attention due to their accurate results in many applications as well as their remarkable generalization capability even with high-dimensional input data. however, SVM classifiers are intrinsically noncontextual, which represents an important limitation in image classification. In this paper, a novel and rigorous framework, which integrates SVMs and Markov random field models in a unique formulation for spatial contextual classification, is proposed. The developed contextual generalization of SVMs, is obtained by analytically relating the Markovian minimum-energy criterion to the application of an SVM in a suitably transformed space. Furthermore, as a second contribution, a novel contextual classifier is developed in the proposed general framework. Two specific algorithms, based on the ho-kashyap and Powell numerical procedures, are combined with this classifier to automate the estimation of its parameters. Experiments are carried out with hyperspectral, multichannel synthetic aperture radar, and multispectral high-resolution images and the behavior of the method as a function of the training-set size is assessed.
In this paper, a short-term load forecasting method is considered, which is based upon a flexible smooth transition autoregressive (STAR) model. The described model is a linear model with time varying coefficients, wh...
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In this paper, a short-term load forecasting method is considered, which is based upon a flexible smooth transition autoregressive (STAR) model. The described model is a linear model with time varying coefficients, which are the outputs of a single hidden layer feedforward neural network. The hidden layer is responsible for partitioning the input space into multiple sub-spaces through multivariate thresholds and smooth transition between the sub-spaces. In this paper, we propose a new method to smartly initialize the weights of the hidden layer of the neural network before its training. A self-organizing map (SUM) network is applied to split the historical data dynamics into clusters, and the ho-kashyap algorithm is then used to obtain the separating planes' equations. Applied to the electricity markets, the proposed method is better able to model the smooth transitions between the different regimes, which are present in the load demand series because of market effects and season effects. We use data from three electricity markets to compare the prediction accuracy of the proposed method with traditional benchmarks and other recent models, and find our results to be competitive. (C) 2011 Elsevier B.V. All rights reserved.
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