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Here we derive achievable bounds on the projected sample size that are free of these two correlation parameters.
We then selected every household along the line in this direction until the projected sample size was achieved.
We anticipated 10% dropout and therefore the projected sample size was set at 179.
The initial projected sample size for this study was 35 patients.
The projected sample size is 100 dissection patients and 100 controls.
The projected sample size of 200 patients aims to detect a clinically important 10% improvement in physical functioning.
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PCA chooses a dimensionality reduction by linear projection that maximizes the scattering of all projected samples.
Fisher discriminant analysis (FDA) is regarded as seeking a direction for which the projected samples are well separated.
Initial matrices are computed by maximizing the centered-kernel alignment (CKA) metric, measuring the affinity between projected samples and labels.
The separating capacity of X can be measured by the total divergence of the projected samples, and the total divergence of projected samples can be expressed by the trace of the covariance of the reflected eigenvector.
PCA generates an orthonormal basis vector that maximizes the scatter of all the projected samples, which is equivalent to find the eigenvalues from the covariance matrix.
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