Kernel methods such as unsupervised MKL, that work batch-based, do not scale well with large numbers of instances, thus its training presents a high computational cost. To ease the burden of computation, we will look at techniques that focus on finding representative instances  within the dataset so as to avoid including the whole number of data entries. This will ideally result in very similar embeddings but with a much lower cost.
Preferred skills: comfortable with Matlab; notions of machine learning.
 E. Elhamifar, G. Sapiro and S. S. Sastry, Dissimilarity-Based Sparse Subset Selection, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 11, pp. 2182-2197, 1 Nov. 2016, doi: 10.1109/TPAMI.2015.2511748.