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In this paper we investigate the potential of subspace techniques, such as, PCA, ICA and NMF in the case of indexing and retrieval of generic 3D models. We address the 3D shape alignment problem via continuous PCA and the exhaustive axis labeling and reflections. We find that the most propitious 3D distance transform leading to discriminative subspace features is the inverse distance transform. Our performance on the Princeton Shape Benchmark is on a par with the state-of-the-art methods.