Why is using $\mathbf{\eta}^T \mathbf{\mu_\mathscr{l}}$ to calculate Fisher's rule easier than using $\mu_\mathscr{l}$? I am currently studying discriminant analysis. Fisher's discriminant $\mathscr{D}$ is defined as follows:

$$\mathscr{D} = \max_{\{ \mathbf{e} \ : \ \vert\vert \mathbf{e} \vert \vert = 1 \}} \mathscr{q} ( \mathbf{e} ) = \max_{\{ \mathbf{e} \ : \ \vert\vert \mathbf{e} \vert \vert = 1 \}} \dfrac{\mathscr{b} ( \mathbf{e} )}{\mathscr{w} ( \mathbf{e} )}$$

where $\mathbf{e}$ is a $d$-dimensional unit vector, $\mathscr{b}$ is the between-class variability, and $\mathscr{w}$ is the within-class variability.
Now, I am told that, if $W$ is invertible, then the following hold:


*

*the between-class variability $\mathscr{b}$ is related to $B$ by $\mathscr{b} ( \mathbf{e} ) = \mathbf{e}^T B \mathbf{e}$;

*the within-class variability $\mathscr{w}$ is related to $W$ by $\mathscr{w}(\mathbf{e}) = \mathbf{e}^T W \mathbf{e}$;

*Fisher's discriminant $\mathscr{D}$ equals the largest eigenvalue of $W^{-1} B$; and

*the unit vector $\mathbf{\eta}$ which maximises the quotient $\mathscr{q}$ is the eigenvector of $W^{-1}B$ which corresponds to $\mathscr{D}$.


I am told that Fisher's rule $\mathcal{R}_F$ is defined as follows:

$$\mathcal{R}_F = \mathscr{l} \ \ \ \ \text{if} \ \ \ \  \vert \mathbf{\eta}^T\mathbf{X} - \mathbf{\eta}^T \mathbf{\mu_{\mathscr{l}}} \vert < \vert \mathbf{\eta}^T \mathbf{X} - \mathbf{\eta}^T \mathbf{\mu_\nu} \vert \ \ \ \ \text{for all $\nu \not= \mathscr{l}$}$$

The following is then said:

Fisher's rule assigns $\mathbf{X}$ the number $\mathscr{l}$ if the scalar $\mathbf{\eta}^T \mathbf{X}$ is closest to the scalar mean $\mathbf{\eta}^T \mathbf{\mu_\mathscr{l}}$. Thus instead of looking for the true mean $\mathbf{\mu_\mathscr{l}}$ which is closest to $\mathbf{X}$, we pick the simpler scalar quantity $\mathbf{\eta}^T \mathbf{\mu_\mathscr{l}}$ which is closest to $\mathbf{\eta^T} \mathbf{X}$.

I am interested in this part:

Thus instead of looking for the true mean $\mathbf{\mu_\mathscr{l}}$ which is closest to $\mathbf{X}$, we pick the simpler scalar quantity $\mathbf{\eta}^T \mathbf{\mu_\mathscr{l}}$ which is closest to $\mathbf{\eta^T} \mathbf{X}$.

Why does using $\mathbf{\eta}^T \mathbf{\mu_\mathscr{l}}$ instead of $\mathbf{\mu_\mathscr{l}}$ make this easier? If $\mathbf{\mu_\mathscr{l}}$ is difficult to calculate, then why would simply multiplying it by $\mathbf{\eta}^T$ suddenly make it easier to calculate? What is the mathematical reasoning behind this?
 A: This material on discriminant analysis is, I think, taken from the book Analysis of Multivariate and High-Dimensional Data by Inge Koch.
I think you are absolutely right that $\eta^T \mu_l$ is no easier to calculate than $\mu_l$ itself. In fact, $\eta^T \mu_l$ requires more calculations to obtain than $\mu_l$. However, I think that the word 'simpler' in the sentence you highlight is just emphasising that $\eta^T \mu_l$ is a scalar rather than a vector.
The author then goes on to explain two advantages of using the scalar $\eta^T \mathbf{X}$ to classify rather than the original vector $\mathbf{X}$...

*

*It reduces/simplifies the multivariate comparisons to univariate comparisons. To classify a vector $\mathbf{X}$ using $\eta^T \mathbf{X}$, we would need $d+\kappa$ arithmetical operations, where $d$ is the dimensionality and $\kappa$ is the number of classes: $d$ operations to compute $\eta^T \mathbf{X}$, then a further $\kappa$ operations for the univariate comparisons. To classify using the original vector $\mathbf{X}$, we would need at least $d\kappa$ operations, given that we'd need to compute $\|\mathbf{X}-\mu_l\|$ for every class. So using $\eta^T \mathbf{X}$ can be much more efficient.


*It gives more weight to the important variables. This is probably a bigger issue. In the two class scenario, classifying based on computing $\|\mathbf{X}-\mu_1\|$ and $\|\mathbf{X}-\mu_2\|$ is equivalent to classifying with $\eta^T \mathbf{X}$ where $\eta=(\mu_1-\mu_2)/\|\mu_1-\mu_2\|$, but this $\eta$ is in general suboptimal, as can be seen from the example below. Finding the optimal $\eta$ can give a much better classifier.
From: Pattern Recognition and Machine Learning by Bishop (Fig 4.6)
Additional details for 1: the univariate comparisons involve computing $|\eta^T \mathbf{X}-\eta^T \mu_l|$ for each class $l$ in turn, making a note of which class $l$ minimises this quantity. The procedure will take longer if there are more classes, but the computation time is not affected by the dimensionality $d$.
Additional details for 2: In the two class scenario, if we classify using $\mathbf{X}$ then we assign $\mathbf{X}$ to class $1$ if $\|\mathbf{X}-\mu_1\|<\|\mathbf{X}-\mu_2\|$. Define $\eta=(\mu_1-\mu_2)/\|\mu_1-\mu_2\|$. Then $\{\eta\}$ is an orthonormal subset of the underlying vector space, so it can be extended to an orthonormal basis for the entire vector space $\{\eta,e_2,\cdots,e_d\}$. So, for $i=1,2$, $$\|\mathbf{X}-\mu_i\|^2=|\eta^T(\mathbf{X}-\mu_i)|^2+\sum_{k=2}^d |e_k^T(\mathbf{X}-\mu_i)|^2$$ Each $e_k$ is orthogonal to $\eta$, so $e_k^T(\mathbf{X}-\mu_1)=e_k^T(\mathbf{X}-\mu_2)$. Therefore $\|\mathbf{X}-\mu_1\|<\|\mathbf{X}-\mu_2\|$ precisely when $|\eta^T(\mathbf{X}-\mu_1)|<|\eta^T(\mathbf{X}-\mu_2)|$. So we're really just classifying using $\eta^T \mathbf{X}$ for this specific suboptimal $\eta$.
