First off, a little disclaimer: I am basing the question on my own interpretation of the problem. It's well possible that I am mistaken.
Setup: We are given: feature vectors $x_1,\ldots, x_k$ with dimension $N$ such that $k \geq N$ and $\sum_{i=1}^k x_i=0$; $N$ orthonormal basis vectors $v_i$ of dimension $N$. We define approximated vectors $x'$ such that $x'_j = \sum_{i}^d v_i^T x_j v_i$ where $j = 1,\ldots ,k$ and $0 < d < N$.
Problem: Show that the following two methods are equivalent.
- minimize the objective function $\sum_{i}^K \|x_i - x'_i\|^2$
- maximize $\sum_{i}^d v_i^T S v_i$ where $S$ is the covariance matrix of the vectors $x$
Question: My understanding is that method 1 minimizes the distance between vector $x$ and its approximation $x'$, and that method 2 selects the features with the highest (co)variance. As I am aware, the eigenvector of the covariance matrix with the highest eigenvalue indicates the axis of highest variance, and that, by implication, a line along that axis would be the Least Squares approximator for the data. Therefore, I see how the methods are equivalent in theory, but I'm having trouble finding a mathematical/formal representation of this intuition based on the given expressions.