Subspace segmentation or clustering can be defined as the process of assigning subspace labels to a set of data points assumed to lie on the union of multiple low-dimensional, linear subspaces. Given that each point can be efficiently expressed using a linear combination of other points from the same subspace, a variety of segmentation algorithms built upon $\ell_1$, nuclear norm, and other convex penalties have recently shown state-of-the-art robustness on multiple benchmarks. However, what if instead of observing the original data points, we instead only have access to transformed, or `twisted’ so to speak, measurements? Here we consider underdetermined affine transformations that may arise in computer vision applications such as bidirectional reflectance distribution function (BRDF) estimation. Unfortunately most existing approaches, convex or otherwise, do not address this highly useful generalization. To fill this void, we proceed by deriving a probabilistic model that simultaneously estimates the latent data points and subspace memberships using simple EM update rules. Moreover, in certain restricted settings this approach is guaranteed to produce the correct clustering. Finally a wide range of corroborating empirical evidence, including a BRDF estimation task, speaks to the practical efficacy of this algorithm.