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...

Centering theory (CT) has been mostly discussed from the point of view of interpretation rather than generation, and research has tended to concentrate on problems of anaphora resolution. This paper examines how centering could fit into the generation task, separating out components of the theory which are concerned with planning and lexical choice. We argue that it is a mistake to define a tot...

‎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...

It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals (Schraudolph and Sejnowski, 1996). Here we generalize this notion to all factors involved in the weight update, leading us to propose centering the slope of hidden unit activatio...

It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals [2]. Here we generalize this notion to all factors involved in the network’s gradient, leading us to propose centering the slope of hidden unit activation functions as well. Slop...

Microtubules play a central role in centering the nucleus or mitotic spindle in eukaryotic cells. However, despite common use of microtubules for centering, physical mechanisms can vary greatly, and depend on cell size and cell type. In the small fission yeast cells, the nucleus can be centered by pushing forces that are generated when growing microtubules hit the cell boundary. This mechanism ...

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...

It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals 2]. Here we generalize this notion to all factors involved in the network's gradient, leading us to propose centering the slope of hidden unit activation functions as well. Slope...