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The problem of finding the eigenvector corresponding to the largest eigenvalue of a stochastic matrix has numerous applications in ranking search results, multi-agent consensus, networked control and data mining. The well known power method is a typical tool for its solution. However randomized methods could be competitors vs standard ones; they require much less calculations for one iteration and...
Normalized cut is a widely used technique for solving a variety of problems. Although finding the optimal normalized cut has proven to be NP-hard, spectral relaxations can be applied and the problem of minimizing the normalized cut can be approximately solved using eigen-computations. However, it is a challenge to incorporate prior information in this approach. In this paper, we express prior knowledge...
The minimum mean square error (MMSE) multiuser detectors (MUDs) based on interference-subspace are deeply investigated, and power iterations for subspace tracking algorithm and a modified MMSE multiuser detector are designed to solve the problems of detecting performance degradation caused by eigenvalue estimation errors. The simulation results show that the proposed modified MMSE detector has faster...
Largely overlooked insights into the convergence behavior of the (N)LMS algorithm focusing on its worst and best case performance is presented. These insights motivate the use of the multigrid paradigm, well known from the numerical solution of partial differential equations, as an important tool in achieving improved convergence speed in (N)LMS-type adaptive filters. We present such a multigrid adaptive...
Certain algorithms and their computational complexity are examined for use in a VLSI implementation of the real-time pattern classifier described in Part I of this work. The most computationally intensive processing is found in the classifier training mode wherein subsets of the largest and smallest eigenvalues and associated eigenvectors of the input data covariance pair must be computed. It is shown...
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