A representation is a way of describing an idea. For example, with generative adversarial networks, a discriminant is a manifold in the space of all images defines images with certain qualities.
Taken from Prof. Hariharan's Linear Algebra Tutorial.
Suppose $A$ is a $m\times n$ matrix where $m > n$ and is full rank.
$$ A = \begin{bmatrix} a_{1,1} & a_{1,2} & \dots & a_{1,n}\\ \vdots & \vdots & \vdots & \vdots\\ \vdots & \vdots & \vdots & \vdots\\ \vdots & \vdots & \vdots & \vdots\\ a_{m,1} & a_{m, 2} & \dots & a_{m ,n} \end{bmatrix} $$
Since there are more constraints then variables, there may not be a solution. Instead, we try to optimize for a solution via least squares.
$$ \min_x \|Ax-b\|^2 $$
We have that
$$ f(x) = \|Ax-b\|^2 = (Ax-b)^T(Ax-b)\\ = (x^TA^T-b^T)(Ax-b)\\ = x^TA^TAx - x^TA^Tb -b^TAx+b^Tb\\ = x^TA^TAx-2 b^TAx + b^Tb. $$