The k-means algorithm is a popular clustering method used in many different fields of computer science, such as data mining, machine learning and information retrieval. However, the k-means algorithm is very likely to converge to some local optimum which is much worse than the desired global

A locally optimum approach for estimating a nonrandom pa-rameter lying in some small neighborhood of a known nom-inal value is considered. Reference is made to a decentral-ized estimation problem in the context of wireless sensor net-works, and particular attention is paid to the design

scenarios. Both a robust formulation of the censoring problem, and a locally-optimum formulation of the censoring problem are considered. We nd conditions under which the robust censoring problem can be solved by designing for the least-favorable distributions from the uncertainty classes. For the locally-optimum

Abstract—We propose a new analytic method for comparing constant gain adaptive signal processing algorithms. Specifically, estimates of the convergence speed of the algorithms allow for the definition of a local measure of performance, called the efficacy, that can be theoretically evaluated

EM algorithm is a very popular iterationbased method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than

Based on local optimality, a novel sequential detection scheme is proposed in this paper. The performance of the proposed scheme is compared with that of the sequential probability ra-tio test (SPRT) and truncated SPRT (TSPRT). The proposed scheme is shown to have higher efficiency, lower

Abstract. We present a novel local optimum based power allocation approach for spectrum sharing in unlicensed frequency bands. The proposed technique is based on the idea of dividing the network in a number of smaller sub-networks or clusters. Sum capacity of each cluster is maximized subject

This is an informal unreviewed manuscript primarily intended for the exchange of information among NMC staff members.

tiply connected networks: When loops are present, the network is no longer singly connected and local propaga tion schemes will invariably run into trouble . We believe there are general undiscovered theorems about the performance of belief propagation on loopy DAGs. These theo rems, which may have

In this article, we study Pareto local optimum sets for the biobjective Traveling Salesman Problem applying straightforward extensions of local search algorithms for the single objective case. The performance of the local search algorithms is illustrated by experimental results obtained for well