In this contribution we extend our previous results on the structured total least squares problem to the case of weighted cost functions. It is shown that the computational complexity of the proposed algorithm is preserved linear in the sample size when the weight matrix is banded with bandwidth that is independent of the sample size.

We proposed a new method to select distinctive features for 3D shape. This approach combines the techniques of neighborhood preserved dimensionality reduction algorithm with clustering method. Based on experimental result, it turns out that the proposed method has better distinctive feature retrieval performance in low dimensional mapping. Using our method, we achieved a reasonably accuracy in ...

A method based on the elementary operations algorithm (EOA) is developed that reduces a system matrix describing a discrete linear repetitive process to a 2-D nonsingular Roesser form such that all the input-output properties, including the transfer-function matrix, are preserved. Some areas for possible future use/application of the developed results will also be briefly discussed.

We show how the QR algorithm can be restructured so that it becomes rich in operations that can achieve near-peak performance on a modern processor. The key is a novel algorithm for applying multiple sets of Givens rotations. We demonstrate the merits of this new QR algorithm for computing the Hermitian (symmetric) eigenvalue decomposition and singular value decomposition of dense matrices when...

In this paper, we introduce a novel colour substitution algorithm based on the dichromatic reflection model. We separate the object colour and scene illumination information in the target area of the original image. The object colour was subsequently substituted while keeping the illumination information unchanged. A new method was employed to estimate the object colour. We also developed a set...

Modular neural networks use a single gating neuron to select the outputs of a collection of agent neurons. Expectation-maximization (EM) algorithms provide one way of training modular neural networks to approximate non-linear functionals. This paper introduces a hybrid interior-point (HIP) algorithm for training modular networks. The HIP algorithm combines an interior-point linear programming (...

Though there are in theory linear-time algorithms for computing runs in strings, recently two of the authors implemented an O(n logn) algorithm to compute runs that was based on the Crochemore’s partitioning repetitions algorithm. The algorithm preserved the running complexity of the underlying Crochemore’s algorithm; however, the static memory requirement – already large at 14n integers for a ...

We present a contraction algorithm for finding a minimal set of arcs or vertices that break all cycles in a digraph. The algorithm is (essentially) an extension to the contraction algorithm for the feedback vertex set problem introduced by Levy and Lew (Levy & Lew 1988). The algorithm’s time complexity of O (m log n) is preserved, while allowing both (weighted) feedback vertices and arcs. As th...