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With a large amount of industrial data available, it is of considerable interest to develop data-based models. The challenge lies in the significant noises that appear in all data collected from industry. The errors-in-variables (EIV) model is a model that accounts for measurement noises in all observations (both input and output). In most of the traditional EIV identification methods, the input generation...
This paper develops three weighted Gaussian process regression (GPR) approaches for multivariate modelling. Taking into account weighted strategy in the traditional univariate GPR, the heteroscedastic noise problem has been solved. The present paper extends the univariate weighted GPR algorithm to the multivariate case. Considering the correlation and weight between data, as well as the correlation...
An outlier is the object which is very different from the rest of the dataset on some measure. Finding such exception has received much attention in the data mining field. In this paper, we propose a KNN based outlier detection algorithm which is consisted of two phases. Firstly, it partitions the dataset into several clusters and then in each cluster, it calculates the Kth nearest neighborhood for...
Recently, spectral clustering has wide application in pattern recognition and data mining because it can obtain global optima solution and adapt to sample spaces with any shape. Thus, a spectral clustering algorithm based on normalized cuts is proposed in this paper. It selects the k eigenvalues and corresponding eigenvectors of a given stochastic matrix and clusters in n times k sub-space. Experimental...
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