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Aman's AI Journal • CS229 • Principal Component Analysis

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We will shortly develop the PCA algorithm. But prior to running PCA per se, typically we first pre-process the data to normalize its mean and variance, as follows: If you found our work useful, please cite it as:

Overview Remarks References Citation Overview In our discussion of factor analysis, we gave a way to model data \(x \in \mathbb{R}^{n}\) as “approximately” lying in some \(k\)-dimensional subspace, where \(k \ll n\). Specifically, we imagined that each point \(x^{(i)}\) was created by first generating some \(z^{(i)}\) lying in the \(k\)-dimensional affine space \(\left\{\Lambda z+\mu ; z \in \mathbb{R}^{k}\right\}\), and then adding \(\Psi\)-covariance noise. Factor analysis is based on a probabilistic model, and parameter estimation used the iterative EM algorithm. In this section, we will de

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