Usually the intrinsic dimension of a signal relates to variables defined in a Cartesian coordinate system. In general, however, it is also possible to describe the concept for non-Cartesian coordinates, for example, using polar coordinates.
Let f(x1, x2) be a two-variable function (or signal) which is of the form
for some one-variable function g which is not constant. This means that f varies, in accordance to g, with the first variable or along the first coordinate. On the other hand, f is constant with respect to the second variable or along the second coordinate. It is only necessary to known the value of one, namely the first, variable in order to determine the value of f. Hence, it is a two-variable function but its intrinsic dimension is one.
A slightly more complicated example is
f is still intrinsic one-dimensional, which can be seen by making a variable transformation
Since the variation in f can be described by the single variable y1 its intrinsic dimension is one.
For the case that f is constant, its intrinsic dimension is zero since no variable is needed to describe variation. For the general case, when the intrinsic dimension of the two-variable function f is neither zero or one, it is two.
In the literature, functions which are of intrinsic dimension zero, one, or two are sometimes referred to as i0D, i1D or i2D , respectively.
For an N-variable function f, the set of variables can be represented as an N-dimensional vector x:
If for some M-variable function g and M × N matrix A is it the case that
then the intrinsic dimension of f is M.
The intrinsic dimension is a characterization of f, it is not an unambiguous characterization of neither g nor A. If the above relation is satisfied for some f, g, and A, it must also be satisfied for the same f and g′ and A′ given by
where B is a non-singular M × M matrix, since
An N variable function which has intrinsic dimension M < N has a characteristic Fourier transform. Intuitively, since this type of function is constant along one or several dimensions its Fourier transform must appear like an impulse (the Fourier transform of a constant) along the same dimension in the frequency space.
Let f be a two-variable function which is i1D. This means that there exists a normalized vector n in R2 and a one-variable function g such that
for all x in R2. If F is the Fourier transform of f (both are two-variable functions) it must be the case that
Here G is the Fourier transform of g (both are one-variable functions), δ is the Dirac impulse function and m is a normalized vector in R2 perpendicular to n. This means that F vanishes everywhere except on a line which passes through the origin of the frequency domain and is parallel to m. Along this line F varies according to G.
Let f be an N-variable function which has intrinsic dimension M, that is, there exists an M-variable function g and M × N matrix A such that
Its Fourier transform F can then be described as follows:
The type of intrinsic dimension described above assumes that a linear transformation is applied to the coordinates of the N-variable function f to produce the M variables which are necessary to represent every value of f. This means that f is constant along lines, planes, or hyperplanes, depending on N and M.
In a general case, f has intrinsic dimension M is there exist M functions a1, a2, ..., aM and an M-variable function g such that
A simple example is transforming a 2-variable function f to polar coordinates:
For the general case, a simple description of either the point sets for which f is constant or its Fourier transform is usually not possible.
The case of a two-variable signal which is i1D appears frequently in computer vision and image processing and captures the idea of local image regions which contain lines or edges. The analysis of such regions has a long history, but it was not until a more formal and theoretical treatment of such operations began that the concept of intrinsic dimension was established, even though the name has varied.
For example, the concept which here is referred to as a image neighborhood of intrinsic dimension 1 or i1D neighborhood is called 1-dimensional by Knutsson (1982), linear symmetric by Bigün & Granlund (1987) and simple neighborhood in Granlund & Knutsson (1995). The term intrinsic dimension appears to be from Zetzsche & Barth (1990).