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contributor authorElmore, Kimberly L.
contributor authorRichman, Michael B.
date accessioned2017-06-09T16:13:33Z
date available2017-06-09T16:13:33Z
date copyright2001/03/01
date issued2001
identifier issn0027-0644
identifier otherams-63690.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4204720
description abstractEigentechniques, in particular principal component analysis (PCA), have been widely used in meteorological analyses since the early 1950s. Traditionally, choices for the parent similarity matrix, which are diagonalized, have been limited to correlation, covariance, or, rarely, cross products. Whereas each matrix has unique characteristic benefits, all essentially identify parameters that vary together. Depending on what underlying structure the analyst wishes to reveal, similarity matrices can be employed, other than the aforementioned, to yield different results. In this work, a similarity matrix based upon Euclidean distance, commonly used in cluster analysis, is developed as a viable alternative. For PCA, Euclidean distance is converted into Euclidean similarity. Unlike the variance-based similarity matrices, a PCA performed using Euclidean similarity identifies parameters that are close to each other in a Euclidean distance sense. Rather than identifying parameters that change together, the resulting Euclidean similarity?based PCA identifies parameters that are close to each other, thereby providing a new similarity matrix choice. The concept used to create Euclidean similarity extends the utility of PCA by opening a wide range of similarity measures available to investigators, to be chosen based on what characteristic they wish to identify.
publisherAmerican Meteorological Society
titleEuclidean Distance as a Similarity Metric for Principal Component Analysis
typeJournal Paper
journal volume129
journal issue3
journal titleMonthly Weather Review
identifier doi10.1175/1520-0493(2001)129<0540:EDAASM>2.0.CO;2
journal fristpage540
journal lastpage549
treeMonthly Weather Review:;2001:;volume( 129 ):;issue: 003
contenttypeFulltext


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