Reviews: What Makes Objects Similar: A Unified Multi-Metric Learning Approach

The main contribution of this paper is to introduce the interesting idea of "activator" (or integrator) operating on multiple metrics. Depending on how this activator (kappa) is picked, one may recover some previously proposed multiple metric learning approaches or create new relevant ones such as those discussed in Section 2.1. The proposed algorithm/analysis is an application of stochastic proximal (sub)gradient descent and is thus not a significant contribution from the optimization point of view (and the presentation of the algorithm is actually quite confusing, see below). My main problem with the paper is the experimental section. I was expecting some experiments showing how the choice of kappa influences the type of metrics that are learned, with an analysis of why some choices are better suited to some kind of problems / datasets / networks.