Topological Data Analysis (TDA) uses techniques from algebraic topology to interpret high-dimensional and noisy data. Both multimodal and multiview Machine Learning (ML) aim to understand complex, high-dimensional data as well via the integration of different data types and different views of the same data type respectively. While their combination seems natural, the methods for implementation, the intent for their fusion, and the data sets used are varied across disciplines. This presentation looks at the ways in which TDA is being fused with multimodal and multiview ML from the approach of a systematized scoping review. In the development of this project, the Kitchenham guidelines and the PRISMA checklist for scoping reviews were followed as closely as possible. Of note is that the limited personnel behind the project means that the guidelines nor the checklist were able to be followed perfectly thus the project is instead a systematized scoping review. The papers reviewed were decided based in part by the inclusion of at least one TDA tool, use of multimodal or multiview data, and implementation of a multimodal or multiview ML framework. Preliminary results compare disciplines involved in research, purposes for development, and the ML pipelines used.
