The use of compression algorithms in machine learning tasks such as clustering and classification has appeared in a variety of fields, sometimes with the promise of reducing problems of explicit feature selection. The theoretical justification for such methods has been founded on an upper bound on Kolmogorov complexity and an idealized information space. An alternate view shows compression algorithms implicitly map strings into implicit feature space vectors, and compressionbased similarity measures compute similarity within these feature spaces. Thus, compression-based methods are not a "parameter free" magic bullet for feature selection and data representation, but are instead concrete similarity measures within defined feature spaces, and are therefore akin to explicit feature vector models used in standard machine learning algorithms. To underscore this point, we find theoretical and empirical connections between traditional machine learning vector models and compression, encouraging cross-fertilization in future work.