PPT-Multidimensional Scaling and Correspondence Analysis

Hair et al 2006 Multivariate Data Analysis Sixth Edition Chapter 9 US Prentice Hall Putu Eka Widya Shanti 29009020 M Satya Oktamalandi 29009021 Learning Objectives. The compelling writings of Amartya Sen partic ipatory poverty exercises in many countries and the Millennium Development Goals MDGs all draw attention to the multiple deprivations suffered by many of the poor and the interconnections among these dep Bob Shackleton. Congressional Budget . Office. Quantitative Linguistics and Dialectology. University of Groningen. June 29, 2012 . Quantifying relative distances between different sounds is a key requirement of . Xavier . Mancero. Statistics Division, ECLAC. Seminar on poverty measurement. Geneva, 5-6 May 2015. Background. Income provides an incomplete assessment of living standards. . Possible bias characterizing poverty . Andrey Ivanov, Senior Policy Advisor, UNDP BRC. Eschborn. , 14 July 2013. Acknowledgements. This presentation summarizes some preliminary results of an ongoing research based on the data come primarily from . input data are ranks: most similar pair AB. <. CB. <. AC most different pair, or input data are rating scales: (very similar 1. . .9 very different), but we don't believe in assuming interval level property of data.. Community Grids Lab.. Indiana University, Bloomington. Seung-Hee Bae. Contents. Multidimensional Scaling (MDS). Scaling by MAjorizing a COmplicated Function (SMACOF). Parallelization of SMACOF. Performance Analysis. Majorization. Algorithm. Seung-Hee. . Bae. , Judy . Qiu. , and Geoffrey Fox. SALSA group in Pervasive Technology Institute. at Indiana University Bloomington. Outline. Motivation. Background and Related Work. Kruskal. , Joseph B. and Myron Wish (1978), . Multidimensional Scaling. , Beverly Hills: Sage. . Great primer to get you started..  Green, Paul E., Frank J. . Carmone. Jr., and Scott M. Smith (1989), . Lachlan Andrew. Joint work with. Minghong. Lin,. . Caltech. Adam . Wierman. , . Caltech. Supported by ARC, NSF. speed. power. greener. faster. Systems must balance delay and energy use .  . Speed scaling. Finding arrays (dimensions) and chunks. Multidimensional scaling. MDS is . a multivariate . data-reduction technique. . Like factor analysis, it is used to tease out underlying relations among a set of observations. . Producing a map of distances from proximities. Goals. Given a matrix of proximities produce a map (preferably two dimensional). Fit is measured by stress – how well the proximities (data) match the distances (map). Scaling. Maya Smith(Winston Salem State University), Anthony Scott (Winston Salem State University), Mentor: Supun . Kamburugamuve . (Indiana University) Principal Investigator: Dr. Geoffrey Fox (Indiana University. academy.zariba.com. 1. Lecture Content. Matrices and Multidimensional Arrays. The . Array. Class. Structs and Array.Sort. 2. Multidimensional Arrays. 3. . An . array. is . multidimensional. if it has more than one . Ali S. Hadi and . Rida. Moustafa. ahadi@aucegypt.edu. ali-hadi@cornell.edu. www.aucegypt.edu/faculty/hadi. Outline of the Talk. Introduction. 2. Types of . Centering and/or Scaling. . Effects of Centering and/or Scaling.