Best-Fit Subspaces

Given an unlabeled dataset, we first define the notion of k-best fitting subspaces as the solution(s) of a minimization task. This is similar to the method of least squares from Chapter 2 , but this time all coordinates of the data points are considered (and not only those designated as labels). By reformulating the initial minimization problem into a maximization problem, we present the greedy algorithm for calculating a best-fit subspace.