Rankings and Preferences: New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications

ISBN-13: 9783662483435 / Angielski / Miękka / 2015 / 91 str.

Joaquim Pinto Da Costa

Opis

This book examines in detail the correlation, more precisely the weighted correlation and applicationsinvolving rankings. A general applicationis the evaluation of methods to predict rankings. Others involverankings representing human preferences to infer user preferences; the use of weighted correlation with microarray data and those in the domain of time series. In this book we presentnewweightedcorrelation coefficients and new methods of weighted principal component analysis.We also introduce new methods of dimension reduction and clustering for time series data and describe some theoretical results on the weighted correlation coefficients in separate sections."

Recenzje

"This book describes newly developed methods of weighted correlation by the author and his collaborators. ... This book is useful for those who want to learn a series of studies on weighted correlations by the author and his collaborators." (Hidehiko Kamiya, Mathematical Reviews, August, 2016)

Spis treści

Introduction.- The Weighted Rank Correlation Coefficient r_W.- The Weighted Rank Correlation Coefficient r_W2 .- A Weighted Principal Component Analysis, WPCA1: Application to Gene Expression Data.- A Weighted Principal Component Analysis (WPCA2) for Time Series Data.- Weighted Clustering of Time Series.- Appendix.- References.

O autorze

Joaquim Pinto da Costa received his first degree in Applied Mathematics from Porto Universitiy (Portugal), his M. Sc. degree in Applied Statistics from Oxford University and his Ph.D. degree in Applied Mathematics from University of Rennes II (France). Since 199, he is Assistant Professor at the Mathematics Department of Porto University. His research interests include Statistics, Statistical Learning Theory, Pattern Recognition, Discriminant Analysis and Clustering, Data Analysis, Neural Networks, SVMs and Machine Learning.