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Oja's principal component analysis (PCA) model is a well-known and powerful technique in the field of signal processing and data analysis. Dynamical behavior of Oja PCA model is an essential issue for practical applications. Existing convergence results are mainly concerned with the case of symmetric covariance matrix. How will Oja model behave when this symmetric condition is violated? In this paper,...
In this paper, we extend the neural network based approaches, which can asymptotically compute the largest or smallest eigenvalues and the corresponding eigenvectors of real symmetric matrix, to the real antisymmetric matrix case. Given any n-by-n real antisymmetric matrix, unlike the previous neural network based methods that were summarized by some ordinary differential equations (ODEs) with 2n...
In this paper a simple infinity norm based neural network algorithm for estimation of the principal component is developed. It seems to be especially useful in applications with changing environment, where the learning process has to be repeated in online manner. Theoretical analysis shows the weight vector converges to the principal eigenvector asymptotically. In comparison with the existing algorithms,...
This paper prepares a review of ICA based approaches that are used for separation of components in functional MRI sequences. In previous works, the FastICA and the Infomax algorithms are investigated in more details; therefore, in this paper we focus on methods such as "radical ICA", "SDD ICA", "Erica" and "Evd" for separation purposes. This comparative study...
A few adaptive algorithms for generalized eigen-decomposition have been proposed, which are very useful in many applications such as digital mobile communications, blind signal separation, etc. These algorithms are all focusing on extracting principal generalized eigenvectors. However, in many practical applications such as dimension reduction and signal processing, extracting the minor generalized...
In this paper, an improved Locally Linear Embedding method is proposed and used in the multi-pose ear recognition. In the traditional LLE method, the neighbors of a data point are selected by using k-nearest neighbor algorithm or e-nearest neighbor algorithm. Both of these methods neglect the different neighborhood of each data point and use a uniform way to select neighbors which leads to the sensitiveness...
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