Comments on: Data integration via analysis of subspaces (DIVAS)

Abstract

We would like to start by saying that this is a very interesting paper that outlines a powerful tool that can be applied in a wide range of areas. In our discussion, we focus on how the novel approach potentially can improve classification results in hard tasks within e.g., medicine and geoscience. In such situations, where it is hard to make precise predictions, it is natural to acquire information from many different sources and formats. The underlying idea is of course that, by utilizing all available information, improvements in terms of e.g., accuracy will be obtained. Although this sounds like a very natural claim, it is not obvious how different sources of information can be incorporated in a useful way. The method described by the authors make headway for many such situations and we would therefore like to congratulate them with a very impressive paper. It would be really interesting to have feedback from the authors concerning natural testbeds for their approach. Would e.g., early detection of cancer utilizing different modalities be possible to use for an evaluation of the method’s performance in practice? In addition, we would like to hear the author’s opinion if their method could be extended to handle data that are not on the form that they include so far in their framework. In particular, it would be interesting to hear if the method could be extended to handle unstructured data like text, which are frequently used in medical applications.