To classify time series by nearest neighbor, we need to specify or learn a distance. We consider several variations of the Mahalanobis distance and the related Large Margin Nearest Neighbor Classification (LMNN). We find that the conventional Mahalanobis distance is counterproductive. However, both LMNN and the class-based diagonal Mahalanobis distance are competitive.

In this work, we propose a scalable and fast algorithm to learn a Mahalanobis distance metric. The key issue in this task is to learn an optimal Mahalanobis matrix in this distance metric. It has been shown in the statistical learning theory [?] that increasing the margin between different classes helps to reduce the generalization error. Hence, our algorithm formulates the Mahalanobis matrix a...

OBJECTIVE To use an electronic nose to identify common upper respiratory bacterial pathogens. STUDY DESIGN Controlled in vitro analysis. METHODS Swabs of bacteria were obtained from in vitro samples. The specimens were vaporized and analyzed over the organic semiconductor-based electronic nose (Cyranose 320). Data from the 32-element sensor array were subjected to principal component analys...

we introduce some new concepts of topological spaces which say α − separable topologicalspace and o-topological group, α − first axiom, α − second axiom, and we find some relations betweenthem with some applications in normed spaces.

This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a new semi-distance for functional observations that generalize the usual Mahalanobis distance for multivariate datasets is introduced. For that, th...

A flexible description of images is offered by a cloud of points in a feature space. In the context of image retrieval such clouds can be represented in a number of ways. Two approaches are here considered. The first approach is based on the assumption of a normal distribution, hence homogeneous clouds, while the second one focuses on the boundary description, which is more suitable for multimo...

A minimum spanning tree (MST) rank histogram (RH) is a multidimensional ensemble reliability verification tool. The construction of debiased, decorrelated, and covariance-homogenized MST RHs is described. Experiments using Euclidean L2, variance, and Mahalanobis norms imply that, unless the number of ensemble members is less than or equal to the number of dimensions being verified, the Mahalano...

current literatures on urban parks and green spaces showed that people’s preference for visiting urban parks in various group users is different. people visit parks for various motives and activities, leading to several patterns of park usability. however, the patterns in urban park use in majority of iran cities are not yet known. this study presented the preference for activity in urban parks...