Generalizing projections to Mahalanobis-type metrics

In this previous post, we defined projections onto a subspace and obtained an expression for the matrix representing this projection. Specifically, if A is a full rank matrix and \text{proj}_A v is the projection of v onto the column space of A, then \text{proj}_A v = Pv, where P = A (A^T A)^{-1} A^T. In this post, we generalize the idea of projections to Mahalanobis-type metrics.

Let M \in \mathbb{R}^{d \times d} be a symmetric positive definite matrix, and define the metric \| \cdot \|_M by \| x \|_M^2 = x^\top Mx for x \in \mathbb{R}^d. Let A \in \mathbb{R}^{d \times k} be some full-rank matrix with k \leq d. We can define the projection onto the column space of A using the metric \| \cdot \|_M, denoted by \Pi, as the matrix such that for all x, \Pi x = Ay, where y \in \mathbb{R}_k minimizes the quantity \| x - Ay \|_M^2. That is, the projection of x is the vector in the column space of A that is closest to x in the metric \| \cdot \|_M.

We can determine the projection matrix \Pi explicitly. Differentiating the objective function by y and setting it to zero,

\begin{aligned} \dfrac{\partial}{\partial y} \| x - Ay \|_M^2 &= \dfrac{\partial}{\partial y} (x - Ay)^\top M (x - Ay) \\  &= -2 A^\top M (x - Ay) = 0, \\  A^\top M A y &= A^\top Mx. \end{aligned}

Since A has full rank and M is non-singular, A^\top M A is non-singular and so y = \left( A^\top M A \right)^{-1} A^\top Mx. This implies that

\begin{aligned} \Pi =  A \left( A^\top M A \right)^{-1} A^\top M. \end{aligned}

How does this relate to our previous post? Setting M = I reduces \Pi to our initial expression for the projection matrix, A (A^\top A)^{-1} A^T. That is, our original expression for the projection matrix was for the (orthogonal) projection according to the standard Euclidean metric.


  1. Ferguson, T. S. (1996). A Course in Large Sample Theory. Chapter 23.

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