Repulsive surfaces

Repulsive Mixtures

Discrete mixtures are used routinely in broad sweeping applications ranging from unsupervised settings to fully supervised multi-task learning. Indeed, finite mixtures and infinite mixtures, relying on Dirichlet processes and modifications, have become a standard tool. One important issue that arises in using discrete mixtures is low separation in the components; in particular, different compon...

Repulsive Affinity-Based Clustering

Clustering is a fundamental problem in machine learning that has applications in a wide variety of domains. It is concerned with finding groups of objects that are closely related under some dissimilarity measure. Most current clustering techniques suffer from a fundamental problem: they do not consider the data density between a point and its corresponding cluster center in the evaluation of t...

Repulsive-SVDD Classification

Support vector data description (SVDD) is a well-known kernel method that constructs a minimal hypersphere regarded as a data description for a given data set. However SVDD does not take into account any statistical distribution of the data set in constructing that optimal hypersphere, and SVDD is applied to solving one-class classification problems only. This paper proposes a new approach to S...

Repulsive Dark Matter

It seems necessary to suppress, at least partially, the formation of structure on subgalactic scales. As an alternative to warm or collisional dark matter, I postulate a condensate of massive bosons interacting via a repulsive interparticle potential, plus gravity. This leads to a minimum lengthscale for bound objects, and to superfluidity. Galactic dynamics may differ significantly from that o...

Clustering with Repulsive Prototypes