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In this paper, an approach for estimating the number of emitters from a set of interleaved pulses trains is proposed. The approach is based on the application of information theoretic criterion, which is formulated by using a new model of eigenvalues from principal component analysis (PCA) of pulse envelope vectors. In this model, the logarithm likelihood function is obtained by clustering the eigenvalues...
Nonlinear principal component analysis (NLPCA) is one of the most progressive computational tools developed during the last two decades. However, in spite of its proper performance in feature extraction and dimension reduction, it is considered as a blind processor which can not extract physical or meaningful factors from dataset. This paper presents a new distributed model of autoassociative neural...
Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications. Aiming at breaking the huge gap between vectors and tensors in conventional statistical tasks, e.g., automatic model selection,...
Density estimation in high-dimensional data spaces is a challenge due to the sparseness of data which is known as ldquothe curse of dimensionalityrdquo. Researchers often resort to low-dimensional subspaces for such tasks, while discard the distribution in the complementary subspace. In this paper, we propose a new mixture density model based on pooled subspace. In our method, the Gaussian components...
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