VC Dimensions of Principal Component Analysis
نویسندگان
چکیده
منابع مشابه
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The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
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ژورنال
عنوان ژورنال: Discrete & Computational Geometry
سال: 2009
ISSN: 0179-5376,1432-0444
DOI: 10.1007/s00454-009-9236-5