The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Audrius Varoneckas - One of the best experts on this subject based on the ideXlab platform.
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CompSysTech - Multidimensional Scaling: multi-objective optimization approach
Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing - CompSysTech '09, 2009Co-Authors: Ausra Mackute-varoneckiene, Antanas Žilinskas, Audrius VaroneckasAbstract:Multidimensional Scaling is a technique for Multidimensional data representation in a low dimensional embedding space. In this paper a multi-objective optimization approach for Multidimensional Scaling using evolutionary algorithms is presented. Two data sets are used for experimental investigation and results show, that this method could be useful for exploratory data analysis.
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Multidimensional Scaling: Multi-Objective Optimization Approach
Stress The International Journal on the Biology of Stress, 2009Co-Authors: Aušra Mackutė-varoneckienė, Antanas Žilinskas, Audrius VaroneckasAbstract:Multidimensional Scaling is a technique for Multidimensional data representation in a low dimensional embedding space. In this paper a multi-objective optimization approach for Multidimensional Scaling using evolutionary algorithms is presented. Two ...
Aušra Mackutė-varoneckienė - One of the best experts on this subject based on the ideXlab platform.
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Multidimensional Scaling: Multi-Objective Optimization Approach
Stress The International Journal on the Biology of Stress, 2009Co-Authors: Aušra Mackutė-varoneckienė, Antanas Žilinskas, Audrius VaroneckasAbstract:Multidimensional Scaling is a technique for Multidimensional data representation in a low dimensional embedding space. In this paper a multi-objective optimization approach for Multidimensional Scaling using evolutionary algorithms is presented. Two ...
Antanas Žilinskas - One of the best experts on this subject based on the ideXlab platform.
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CompSysTech - Multidimensional Scaling: multi-objective optimization approach
Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing - CompSysTech '09, 2009Co-Authors: Ausra Mackute-varoneckiene, Antanas Žilinskas, Audrius VaroneckasAbstract:Multidimensional Scaling is a technique for Multidimensional data representation in a low dimensional embedding space. In this paper a multi-objective optimization approach for Multidimensional Scaling using evolutionary algorithms is presented. Two data sets are used for experimental investigation and results show, that this method could be useful for exploratory data analysis.
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Multidimensional Scaling: Multi-Objective Optimization Approach
Stress The International Journal on the Biology of Stress, 2009Co-Authors: Aušra Mackutė-varoneckienė, Antanas Žilinskas, Audrius VaroneckasAbstract:Multidimensional Scaling is a technique for Multidimensional data representation in a low dimensional embedding space. In this paper a multi-objective optimization approach for Multidimensional Scaling using evolutionary algorithms is presented. Two ...
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two level minimization in Multidimensional Scaling
Journal of Global Optimization, 2007Co-Authors: Antanas Žilinskas, Julius ŽilinskasAbstract:Multidimensional Scaling with city block norm in embedding space is considered. Construction of the corresponding algorithm is reduced to minimization of a piecewise quadratic function. The two level algorithm is developed combining combinatorial minimization at upper level with local minimization at lower level. Results of experimental investigation of the efficiency of the proposed algorithm are presented as well as examples of its application to visualization of Multidimensional data.
Adrian E Raftery - One of the best experts on this subject based on the ideXlab platform.
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bayesian Multidimensional Scaling and choice of dimension
Journal of the American Statistical Association, 2001Co-Authors: Mansuk Oh, Adrian E RafteryAbstract:Multidimensional Scaling is widely used to handle data that consist of similarity or dissimilarity measures between pairs of objects. We deal with two major problems in metric Multidimensional Scaling–configuration of objects and determination of the dimension of object configuration–within a Bayesian framework. A Markov chain Monte Carlo algorithm is proposed for object configuration, along with a simple Bayesian criterion, called MDSIC, for choosing their dimension. Simulation results are presented, as are real data. Our method provides better results than does classical Multidimensional Scaling and ALSCAL for object configuration, and MDSIC seems to work well for dimension choice in the examples considered.
Craig L Frisby - One of the best experts on this subject based on the ideXlab platform.
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confirmatory factor analysis and profile analysis via Multidimensional Scaling
Multivariate Behavioral Research, 2007Co-Authors: Mark L Davison, Craig L FrisbyAbstract:This paper describes the Confirmatory Factor Analysis (CFA) parameterization of the Profile Analysis via Multidimensional Scaling (PAMS) model to demonstrate validation of profile pattern hypotheses derived from Multidimensional Scaling (MDS). Profile Analysis via Multidimensional Scaling (PAMS) is an exploratory method for identifying major profiles in a multi-subtest test battery. Major profile patterns are represented as dimensions extracted from a MDS analysis. PAMS represents an individual observed score as a linear combination of dimensions where the dimensions are the most typical profile patterns present in a population. While the PAMS approach was initially developed for exploratory purposes, its results can later be confirmed in a different sample by CFA. Since CFA is often used to verify results from an exploratory factor analysis, the present paper makes the connection between a factor model and the PAMS model, and then illustrates CFA with a simulated example (that was generated by the PAMS m...
