We propose a statistical non-parametric classification of brain tissues from an MR image based on the voxel intensities and on the relative anatomical location of the different tissues. Classically, the overlap of the tissue probability distribution functions for voxel intensities can be reduced by using multi-component (T1w,T2w,Pd,...) MR images, but at a much higher cost for image acquisition...

We collect data on grain and salt prices, as well as language, for more than 200 South Asian markets in the 19th and early 20th centuries. Conditional on a rich set of controls and fixed effects, we find that linguistically distant markets are less integrated as measured by the degree of price correlation. While linguistically distant markets exhibit greater genetic distance, greater difference...

In this work we introduce a new distance estimation technique by boosting and we apply it to the K-Nearest Neighbor Classifier (KNN). Instead of applying AdaBoost to a typical classification problem, we use it for learning a distance function and the resulting distance is used into K-NN. The proposed method (Boosted Distance with Nearest Neighbor) outperforms the AdaBoost classifier when the tr...

We quantitatively analyze the performance of exact and approximate nearest-neighbors algorithms on increasingly high-dimensional problems in the context of sampling-based motion planning. We study the impact of the dimension, number of samples, distance metrics, and sampling schemes on the efficiency and accuracy of nearest-neighbors algorithms. Efficiency measures computation time and accuracy...

A distance transform (DT) converts a binary image consisting of foreground (feature) and background (non-feature) pixels into a gray level image where each pixel contains the distance from the corresponding pixel to the nearest foreground pixel. The computation of the exact Euclidean DT is computationally complex task and, therefore, approximations are typically utilized. In this paper, an area...

W ABSTRACT e describe a bisection method to determine the 2-norm and Frobenius norm-g distance from a given matrix A to the nearest matrix with an eigenvalue on the ima inary axis. If A is stable in the sense that its eigenvalues lie in the open left half e plane, then this distance measures how "nearly unstable" A is. Each step provides ither a rigorous upper bound or a rigorous lower bound on...

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, the nearest neighbor search step of this algorithm can be computationally expensive, as the distance between each input vector and all cluster centers need to be calculated. To accelerate this step, a computationally inexpensive distance estimation method can be tried first, resulting in the rejection o...

Constrained k nearest neighbor query for uncertain object in the network is to find k uncertain objects which are the k nearest neighbors with range constraint of the query object in the network. For solving this problem, the uncertain object is modeled as the fuzzy object and the network  -distance between fuzzy objects in the network is defined. Base on them, the concept of constrained k nea...

Finding Nearest Neighbors efficiently is crucial to the design of any nearest neighbor classifier. This paper shows how Layered Range Trees could be used for efficient nearest neighbor classification. The presented algorithm is simple and finds the nearest neighbor in a logarithmic order. It performs d log n + k distance measures to find the nearest neighbor, where k is a constant that is much ...