PCA

TL;DR The usual procedure to calculate the $d$-dimensional principal component analysis consists of the following steps: Calculate average $$ \bar{m}=\sum\_{i=1}^{N} m_{i} \in \mathbb{R} $$ data matrix $$ \mathbf{M}=\left(m\_{1}-\bar{m}, \ldots, m\_{N}-\bar{m}\right) \in \mathbb{R}^{d \times \mathrm{N}} $$ scatter matrix (covariance matrix) $$ \mathbf{S}=\mathbf{M M}^{\mathrm{T}} \in \mathbb{R}^{d \times d} $$ of all feature vectors $m\_{1}, \ldots, m\_{N}$

2020-11-07