It seems to me that feature vectors in ML are used approximately like that, so I don't think it's so inappropriate. Take the preponderance of techniques like PCA, for example, features tend to be very often treated as rotation-invariant. Even if it's not 100% the case in the raw data, one very often wants to learn whatever features lie in subspaces that are invariant to linear transformations.