Linear Discriminant Analysis Newbie question Does linear discriminant analysis always project the points to a line? Most of the graphical illustrations of LDA that I see online  use an example of 2 dimensional points which are projected onto a straight line y=mx+c. If the points were each a 10-dimensional vector, does LDA still project them to a line?
Or would it project them to a hyperplane with 9 dimensions or less.
ANother question about projections: If I have a vector Y=[a,b,c,d]. The projection of this vector onto a given line is the product of the direction vector V of the line and the vector Y. This is equivalent to a dot product given by transpose(V).Y, and gives just one number (a scalar).
This seems to be the way how LDA works. So, if I may ask, does LDA map a full n-dimensional vector onto a scalar (a singe number)?
Apologies in advance for my newbie question.
 A: LDA seeks to reduce dimensionality while preserving as much of the class discriminatory information as possible. Assume we have a set of $d$-dimensional observations $X$, belonging to $C$ different classes. The goal of LDA is to find an linear transformation (projection) matrix $L$ that converts the set of labelled observations $X$ into another coordinate system $Y$ such that the class separability is maximized. The dataset is transformed into the new subspace as:
\begin{equation}
Y = XL
\end{equation}
The columns of the matrix $L$ are a subset of the $C-1$ largest (non-orthogonal) eigenvectors of the  squared matrix $J$, obtained as:
\begin{equation}
J = S_{W}^{-1} S_B
\end{equation}
where $S_W$ and $S_B$ are the scatter matrices within-class and respectively between-classes. 
When it comes to dimension reduction in LDA, if some eigenvalues have a significantly bigger magnitude than others then we might be interested in keeping only those dimensions, since they contain more information about our data distribution. 
This becomes particularly interesting as $S_B$ is the sum of $C$ matrices of rank $\leq 1$, and the mean vectors are constrained by $\frac{1}{C}\sum_{i=1}^C \mu_i = \mu$ \cite{c.radhakrishnarao1948}. Therefore, $S_B$ will be of rank $C-1$ or less, meaning that there are only $C-1$ eigenvalues that will be non-zero (more info here). For this reason, even if the dimensionality $k$ of the sub-space $Y$ can be arbitrarily chosen, it does not make any sense to keep more than $C-1$ dimensions, as they will not carry any useful information.  In fact, in \ac{lda} the smallest $d - (C-1)$ dimensions have magnitude zero, and therefore the subspace $Y$ should have exactly $k = C-1$ dimensions.
A: LDA project to (at most) $n_{classes} - 1$ dimensions, so binary (2-class) LDA reduces to 1D (= onto line).
10 classes would lead to a 9D projection (as long as X is at least 9D, of course).  

  does LDA map a full n-dimensional vector onto a scalar (a singe number)?
  Not always, see above.

For more details on what the projection step does, see e.g.  https://stats.stackexchange.com/a/87509/4598
(Obviously, if you code your classes as numbers then the final class prediction will be a single number)
