منابع مشابه
Clustering ; Single Linkage ; and Pairwise Distance Concentration
But what about high dimensions? What is the density of the points near the mean? And how far away is the average point from it’s component mean? Let us address this questions for a single isotropic Gaussian distribution. First, note that E[‖x‖] = nσ. Hence, on average, we expect a point to be rather far from mean, but let us quantify this. Recall, that the distribution of ‖x‖ is a χn distributi...
متن کاملDistance Metric Learning from Pairwise Proximities
We compare techniques for embedding a data set into Euclidean space under different notions of proximity constraints.
متن کاملFast Neighborhood Subgraph Pairwise Distance Kernel
We introduce a novel graph kernel called the Neighborhood Subgraph Pairwise Distance Kernel. The kernel decomposes a graph into all pairs of neighborhood subgraphs of small radius at increasing distances. We show that using a fast graph invariant we obtain significant speed-ups in the Gram matrix computation. Finally, we test the novel kernel on a wide range of chemoinformatics tasks, from anti...
متن کاملLearning Texture Similarity with Perceptual Pairwise Distance
In this paper, we demonstrate how texture classification and retrieval could benefit from learning perceptual pairwise distance of different texture classes. Textures as represented by certain image features may not be correctly compared in a way that is consistent with human perception. Learning similarity helps to alleviate this perceptual inconsistency. For textures, psychological experiment...
متن کاملDesigning Networks with Bounded Pairwise Distance
We study the following network design problem: Given a communication network, nd a minimum cost subset of missing links such that adding these links to the network makes every pair of points within distance at most d from each other. The problem has been studied earlier 17] under the assumption that all link costs as well as link lengths are identical, and was shown to be (log n)-hard for every...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications in Statistics - Theory and Methods
سال: 2020
ISSN: 0361-0926,1532-415X
DOI: 10.1080/03610926.2020.1741626