Principal component analysis [Jaeyoung Wiki]

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====== Principal component analysis ======

Principal component analysis is essentially boiling down multidimensional data with a lot of dimensions (aka columns) into a few dimensions while keeping **most** of the information.

Given $n$ $m$-dimensional vectors, steps to find the top $k$ principal components:

  - Calculate the component-wise average of all of the vectors $\bar{x} = \frac{1}{n} \sum_{i=1}^n x_i $
  - Form $m \times m$ matrix $S = \frac{1}{n}\sum_{i=1}^n (x_i - \bar{x})(x_i - \bar{x})^T $
  - Calculate the $m$-dimensional eigenvectors associated with the largest $k$ eigenvalues of $S$: $v_1, ... v_k$ associated with $\lambda_1, ..., \lambda_k$
  - The $k$ dimensional representation of $x_i$ is then $\hat{x}_i = (x_i^Tv_1, ... x_i^Tv_k)$

Another way to state the objective:

$$ \min \sum_{i=1}^n || x_i - \hat{x}_i ||^2 $$

$$ \max \sum_{i=1}^n || \hat{x}_i ||^2 $$