First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper generalizes multi-kernel k-means clustering (solved via relaxation) to the case where each clustered item (here, a person) gets an item-specific set of weights over the multiple kernels, rather than the traditional, shared, global weighting of the kernels. Using TCGA (cancer) data, with 3 modalities, they demonstrate that this generalization yields better clusterings than the traditional (global approach), when measured against 3 bronze standard clusterings arising from known clinical clusters. The writing is clear, making for an easy read. Although this is a somewhat incremental-seeming tweak, I think it was clever, with the potential to actually be used (rather than lost in the NIPS archives), and therefore of some significance. Other comments: In the introduction you mention that k-means is susceptible to local minima, and then use this to motivate the relaxation approach.

Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications. Existing models usually assume simple inter-dependence among variables, space, and time, and are computationally expensive. We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis, which can conveniently incorporate different properties in spatio-temporal data, such as spatial clustering and shared structure among variables. We demonstrate how the general framework can be applied to cokriging and forecasting tasks, and develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantee. We conduct experiments on both synthetic datasets and real application datasets to demonstrate that our method is not only significantly faster than existing methods but also achieves lower estimation error.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

However, BO suffers from the so called'cold start' problem [