The performance of nearest neighbor methods is degraded by the presence of hubs, i.e., objects in the dataset that are similar to many other objects. In this paper, we show that the classical method of centering, the transformation that shifts the origin of the space to the data centroid, provides an effective way to reduce hubs. We show analytically why hubs emerge and why they are suppressed ...

This work analyzes centered Restricted Boltzmann Machines (RBMs) and centered Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centered and normal Boltzmann Machines (BMs) and thus RBMs and DBMs are different parameterizations of the same model class, such that any normal BM/RBM/DBM can be trans...

‎a full nesterov-todd (nt) step infeasible interior-point algorithm‎ ‎is proposed for solving monotone linear complementarity problems‎ ‎over symmetric cones by using euclidean jordan algebra‎. ‎two types of‎ ‎full nt-steps are used‎, ‎feasibility steps and centering steps‎. ‎the‎ ‎algorithm starts from strictly feasible iterates of a perturbed‎ ‎problem‎, ‎and, using the central path and feasi...

Recently there have been a number of works that model the zero pronoun resolution with the concept called `center.' However, the usefulness of the previous centering frameworks has not fully evaluated with naturally occurring discourses. Furthermore, the previous centering theory has handled only the phenomena in successive simple sentences and has not adequately addressed the way to handle com...

A recent paper (Beaver, 2004) recasts the Centering algorithm for pronoun resolution (Brennan et al., 1987) in terms of optimality theory (OT). Although the limitations of centering for pronoun resolution are well known, the algorithm's restatement in OT highlights the strengths of centering and produces an algorithm whose behavior is more transparent to the developer. The authors' motivation f...