Titlebook: Rankings and Preferences; New Results in Weigh Joaquim Pinto da Costa Book 2015 The Author(s) 2015 microarray data.principal component anal

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The Weighted Rank Correlation Coefficient ,, using a linear function of those ranks in the case of . and a quadratic function in the case of .. In both cases, these functions give more importance to top ranks than lower ones, although . has some advantages over . as we will see. In some of the applications of weighted correlation, ties can ha

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A Weighted Principal Component Analysis, WPCA1; Application to Gene Expression Data, WPCA1 and compare it with the usual PCA, we consider the problem of analyzing gene expression datasets. In the second part of this chapter, we propose a new PCA-based algorithm to iteratively select the most important genes in a microarray dataset. We show that this algorithm produces better result

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Joaquim Pinto da Costaeld of natural hazards. From geology and geophysics to engineering and emergency response to social psychology and economics, the study of natural hazards draws input from an impressive suite of unique and prev978-1-4020-4399-4Series ISSN 1388-4360 Series E-ISSN 1871-756X

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Joaquim Pinto da Costaeld of natural hazards. From geology and geophysics to engineering and emergency response to social psychology and economics, the study of natural hazards draws input from an impressive suite of unique and prev978-1-4020-4399-4Series ISSN 1388-4360 Series E-ISSN 1871-756X

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