Principal Components Analysis of Multispectral Image Data
نویسندگان
چکیده
منابع مشابه
Integrating Data Transformation in Principal Components Analysis.
Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-b...
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Derivation of PCA I: For a set of d-dimensional data vectors {x}i=1, the principal axes {e}qj=1 are those orthonormal axes onto which the retained variance under projection is maximal. It can be shown that the vectors ej are given by the q dominant eigenvectors of the sample covariance matrix S, such that Sej = λjej . The q principal components of the observed vector xi are given by the vector ...
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ژورنال
عنوان ژورنال: Microscopy Today
سال: 2004
ISSN: 1551-9295,2150-3583
DOI: 10.1017/s1551929500056297