Determining The Number of Principal Components with Schur's Theorem in Principal Component Analysis
Cihan KARAKUZULU,
İbrahim Halil GÜMÜŞ,
Serkan GÜLDAL
et al.
Abstract:Principal Component Analysis is a method for reducing the dimensionality of datasets while also limiting information loss. It accomplishes this by producing uncorrelated variables that maximize variance one after the other. The accepted criterion for evaluating a Principal Component’s (PC) performance is λ_j/tr(S) where tr(S) denotes the trace of the covariance matrix S. It is standard procedure to determine how many PCs should be maintained using a predetermined percentage of the total variance. In this stud… Show more
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