Given (finite dimensional?) vector spaces $V$ and $W$, we can define the transpose of a linear map $f:V \to W$ by the obvious map $W^* \to V^*$. Can we do a similar thing for the determinant? Can we define the determinant of an operator without making reference to a matrix, or bases? I feel we should be able to do this, given that the value of the determinant is independent of the basis chosen.
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The determinant of a linear map $f:V\to W$ between finite dimensional vector spaces over a field $K$ can only be defined if $V=W \; !$
The intrinsic definition is then as follows :
This approch (pioneered by Bourbaki around 1950, I think) is undoubtedly very elegant, but it requires a knowledge of multilinear algebra not taught in elementary undergraduate courses.
Elaborating on Sanchez's answer, it's useful to consider the action of a linear operator on objects created by wedge products--which represent oriented planes, volumes, and so on, just as a single vector represents an oriented line.
We generally define the action of linear operators across the wedge product as follows:
$$\underline T(a \wedge b) \equiv \underline T(a) \wedge \underline T(b)$$
The operator on a wedge product is the wedge product of the operator on the individual vectors.
Now, consider the highest-dimensional object that can be formed by wedges. In an $N$-dimensional space, this is a wedge product of $N$ linearly independent vectors. This object itself forms a 1d vector space---all objects of this kind are scalar multiples of each other. For this reason, this object is often called the pseudoscalar. We'll call it $i_N$.
Now, what is $\underline T(i_N)$? First, linear operators that can be extended across wedges preserve the grade of their arguments--vectors go to vectors, planes to go planes, and the pseudoscalar can only go to (some multiple of) itself.
In other words,
$$\underline T(i_N) = \alpha i_N$$
We call the number $\alpha$ the determinant, and it describes how a unit volume in the space is dilated or shrunk by the linear operator. There is no need to resort to a matrix representation to do this, nor must we choose a specific basis to know this is so.