Exact Principal Component Influence Measures Applied to the Analysis of Spectroscopic Data on Rice

Autor:	B. J. A. Mertens
Rok vydání:	1998
Předmět:	Statistics and Probability Euclidean distance Normalization (statistics) Chemometrics Clustering high-dimensional data Euclidean space Statistics Principal component analysis Euclidean geometry Applied mathematics Statistics Probability and Uncertainty Eigenvalues and eigenvectors Mathematics
Zdroj:	Journal of the Royal Statistical Society Series C: Applied Statistics. 47:527-542
ISSN:	1467-9876 0035-9254
DOI:	10.1111/1467-9876.00126
Popis:	SUMMARY Exact influence measures are applied in the evaluation of a principal component decomposition for high dimensional data. Some data used for classifying samples of rice from their near infra-red transmission profiles, following a preliminary principal component analysis, are examined in detail. A normalization of eigenvalue influence statistics is proposed which ensures that measures reflect the relative orientations of observations, rather than their overall Euclidean distance from the sample mean. Thus, the analyst obtains more information from an analysis of eigenvalues than from approximate approaches to eigenvalue influence. This is particularly important for high dimensional data where a complete investigation of eigenvector perturbations may be cumbersome. The results are used to suggest a new class of influence measures based on ratios of Euclidean distances in orthogonal spaces.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::eb074031f1593e4d4a1737f3bd4dc2f2 https://doi.org/10.1111/1467-9876.00126 Zobrazit plný text záznamu Plný text ve formátu PDF