- Section 4 is very tersely written (maybe due to limitations in space) and could have benefitted with a slower development for an easier read.
- Issues of convergence, especially when applying gradient descent over a non-Euclidean space, is not addressed
In all, a rather thorough paper that derives an efficient way to compute gradients for optimization on LDSs modeled using extended subspaces and kernel-based similarity. At one hand, this leads to improvements over some competing methods. Yet, at its core, the paper avoids handling of the harder topics including convergence and any analysis of the proposed optimization scheme. None the less, the derivation of the gradient computations is interesting by itself. Hence, my recommendation.
- Section 4 is very tersely written (maybe due to limitations in space) and could have benefitted with a slower development for an easier read.