FLDA Ideas

• $S_{B}, S_{W}$: scatter matrices (estimate covariance)
• $W$: projection matrix from $D$ to $K$ dimensions

Objective: maximize

$$J(W) = \displaystyle\frac{|W^{T}S_{B}W|}{|W^{T}S_{W}W|}$$

• eigenvalue scenario instead of gradient descent