Abstract Estimating the elements of a matrix, when only margins (row and column sums) are known, but supposedly similar ‘reference matrix’ is available, standard problem in many disciplines. After discussing main types, issues applications these two-directional matrix adjustment problems paper concentrates on case negative models with quadratic objective functions. The solution Improved Normali...

The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to av...

The normalized least-mean-square (NLMS) adaptive filter is widely used in system identification. In this paper, we develop an optimized NLMS algorithm, in the context of a state variable model. The proposed algorithm follows a joint-optimization problem on both the normalized step-size and regularization parameters, in order to minimize the system misalignment. Consequently, it achieves a prope...

The transductive SVM is a semi-supervised learning algorithm that searches for a large margin hyperplane in feature space. By withholding the training labels and adding a constraint that favors balanced clusters, it can be turned into a clustering algorithm. The Normalized Cuts clustering algorithm of Shi and Malik, although originally presented as spectral relaxation of a graph cut problem, ca...

In this paper, we propose improved versions of normalized least mean square (NLMS) algorithm: single and multiple -variable step size normalized least mean square (VSSNLMS) algorithms for echo cancellation. The presented algorithms exhibit faster convergence rate in comparison to NLMS algorithm. Simulation results employing standard figure of merits show how the algorithms perform better than N...

Employing a recently introduced framework, within which a large number of classical and modern adaptive lter algorithms can be viewed as special cases, a generic, variable step-size adaptive lter has been presented. Variable Step-Size (VSS) Normalized Least Mean Square (VSSNLMS) and VSS A ne Projection Algorithms (VSSAPA) are particular examples of adaptive algorithms covered by this generic va...