The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C1, . . . , Ck ⊆ P such that k i=1 p,q∈Ci d(p, q) is minimized. We show the first effi...
We propose a new transductive learning algorithm for learning optimal linear representations that utilizes unlabeled data. We pose the problem of learning linear representations a...
Abstract. Traditional clustering algorithms are based on one representation space, usually a vector space. However, in a variety of modern applications, multiple representations ex...
Karin Kailing, Hans-Peter Kriegel, Alexey Pryakhin...
Using multiple reference images in 3D image warping has been a challenging problem. Recently, the Layered Depth Image (LDI) was proposed by Shade et al. to merge multiple referenc...
Supporting visual analytics of multiple large-scale multidimensional datasets requires a high degree of interactivity and user control beyond the conventional challenges of visual...