In multivariate analysis we often deal with situations involving several populations, such as discriminant analysis, where the assumption of equality of scatter matrices is usually assumed. Yet sometimes, this assumption is not adequate but problems related to an excessive number of parameters will arise if we estimate the scatter matrices separately for each population. In many practical situa...

Preference aggregation in Group Decision Making (GDM) is a substantial problem that has received lot of research attention. problems involving fuzzy preference relations constitute an important class within GDM. Legacy approaches dealing with the latter type can be classified into indirect approaches, which involve deriving group matrix as intermediate step, and direct deduce ranking based on i...

The matrix of evolutionary distances is a model-based statistic, derived from molecular sequences, summarizing the pairwise phylogenetic relationships between a collection of species. Phylogenetic tree reconstruction methods relying on this matrix are relatively fast and thus widely used in molecular systematics. However, because of their intrinsic reliance on summary statistics, distance-matri...

The matrix of evolutionary distances is a model-based statistic, derived from molecular sequences, summarizing the pairwise phylogenetic relations between a collection of species. Phylogenetic tree reconstruction methods relying on this matrix are relatively fast and thus widely used in molecular systematics. However, because of their intrinsic reliance on summary statistics, distance-matrix me...

Document clustering is very important in the field of text categorization. Genetic algorithm, which is an optimization based technique which can be applied for finding out the best cluster centres easily by computing fitness values of data points. While clustering around weighted prototype technique is especially helpful when proper pairwise similarities are available. This technique does not f...

Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on m...

Robert Schober , Wolfgang H. Gerstacker , and Lutz H.–J. Lampe Department of Electrical and Computer Engineering, University of Toronto e-mail: rschober @comm.utoronto.ca Lehrstuhl für Nachrichtentechnik II, Universität Erlangen–Nürnberg Abstract — In this paper, space–time block–coded transmission over fading intersymbol interference (ISI) channels is investigated. A lower bound for the pairwi...

Pairwise clustering methods partition the data space into clusters by the pairwise similarity between data points. The success of pairwise clustering largely depends on the pairwise similarity function defined over the data points, where kernel similarity is broadly used. In this paper, we present a novel pairwise clustering framework by bridging the gap between clustering and multi-class class...

As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling m...