Why the objective function in Fisher Discriminant Analysis? I know FDA wants to find some linear combination $z = W^\top x$ so that the projected data has maximum between-class covariance and minimum within-class covariance. The first thing that came to my mind is to formulate the objective function in terms of Frobenius norm like the following,
$$
\max_W \frac{\|W^\top S_b W\|_F}{\|W^\top S_w W\|_F}
$$
which makes sense to me, because you want to measure the sum of all covariances in absolute value.
But it doesn't seem to reduce to the following two formulations most known in the literature,
the first formulation
$$
\max_W \frac{\operatorname{tr}(W^T S_b W)}{\operatorname{tr}(W^T S_w W)}
$$
and the second formulation
$$
\max_W \frac{|W^\top S_b W|}{|W^\top S_w W|}
$$
where $S_b$ is the between-class scatter matrix and $S_w$ is the within-class scatter matrix.
And let's suppose we're projecting on to $l \lt K - 1$ dimensional subspace, where $K$ is the number of classes. Thus in both criterion, $W$ is of shape $p \times l$, where $p$ is the number of covariates.

*

*I don't know the exact idea behind these two formulations, why we can formulate FDA problem in these two ways


*and I don't see the equivalence between the two except the resulting solution is the same.
 A: The answer is that, for any square matrix $A$, the trace is the sum of eigenvalues, that is,
$$
\operatorname{tr}(A) = \sum \lambda_i,
$$
and determinant is the product of eigenvalues, i.e,
$$
\det(A) = \prod \lambda_i.
$$
So maximizing the trace or determinant corresponding the eigenvalues of $A$.
I think that using the Forbenius norm as the objective function is incorrect. To make sure that we understand thoroughly, we go through the basics of covariance matrix. Consider the data matrix $\mathbf{X}$, with each column as the observation of the same data, and each row as a measurement type. The covariance matrix is defined as
$$
\mathbf{C}_\mathbf{X} = \frac{1}{n} \mathbf{XX}^T.
$$
The element $c_{ij}$ of $\mathbf{C}$ measures the covariance of the $i$-th and the $j$-th measurement types, and when $i=j$ we call it variance of measurement type $i$. Principal component analysis (PCA) aims at **maximize the variance maximizes $\mathbf{C_X}$'s diagonal value, that is, the variance, while minimizes the off-diagonal value. Since $\mathbf{C_X}$ is diagonalizable,  Its objective function is formulated as
$$
\begin{gathered}
\max_\mathbf{W} \operatorname{tr}(\mathbf{W}^T\mathbf{C_X} \mathbf{W}) \\
\text{s.t. } \mathbf{W}^T \mathbf{W} = I.
\end{gathered}
$$
or
$$
\begin{gathered}
\max_\mathbf{W} \det(\mathbf{W}^T\mathbf{C_X} \mathbf{W}) \\
\text{s.t. } \mathbf{W}^T \mathbf{W} = I.
\end{gathered}
$$
Back to the Fisher discriminant analysis is to maximize the between-class variance and minimize the within-class variance, thus its objective function is formulated as
$$
\begin{gathered}
\max_\mathbf{W} \frac{\operatorname{tr}(\mathbf{W}^T\mathbf{S}_b \mathbf{W})}{\operatorname{tr}(\mathbf{W}^T\mathbf{S}_w \mathbf{W})} \\
\text{s.t. } \mathbf{W}^T \mathbf{W} = I.
\end{gathered}
$$
or
$$
\begin{gathered}
\max_\mathbf{W} \frac{\det(\mathbf{W}^T\mathbf{S}_b \mathbf{W})}{\det(\mathbf{W}^T\mathbf{S}_w \mathbf{W})} \\
\text{s.t. } \mathbf{W}^T \mathbf{W} = I.
\end{gathered}
$$
Since a Forbenius norm is
$$
\|\mathrm{A}\|_F \equiv \sqrt{\sum_{i=1}^m \sum_{j=1}^n\left|a_{i j}\right|^2},
$$
it takes all elements in $A$ into account. It doesn't make sense.
