44,625 research outputs found
Reverse k Nearest Neighbor Search over Trajectories
GPS enables mobile devices to continuously provide new opportunities to
improve our daily lives. For example, the data collected in applications
created by Uber or Public Transport Authorities can be used to plan
transportation routes, estimate capacities, and proactively identify low
coverage areas. In this paper, we study a new kind of query-Reverse k Nearest
Neighbor Search over Trajectories (RkNNT), which can be used for route planning
and capacity estimation. Given a set of existing routes DR, a set of passenger
transitions DT, and a query route Q, a RkNNT query returns all transitions that
take Q as one of its k nearest travel routes. To solve the problem, we first
develop an index to handle dynamic trajectory updates, so that the most
up-to-date transition data are available for answering a RkNNT query. Then we
introduce a filter refinement framework for processing RkNNT queries using the
proposed indexes. Next, we show how to use RkNNT to solve the optimal route
planning problem MaxRkNNT (MinRkNNT), which is to search for the optimal route
from a start location to an end location that could attract the maximum (or
minimum) number of passengers based on a pre-defined travel distance threshold.
Experiments on real datasets demonstrate the efficiency and scalability of our
approaches. To the best of our best knowledge, this is the first work to study
the RkNNT problem for route planning.Comment: 12 page
Demystifying Fixed k-Nearest Neighbor Information Estimators
Estimating mutual information from i.i.d. samples drawn from an unknown joint
density function is a basic statistical problem of broad interest with
multitudinous applications. The most popular estimator is one proposed by
Kraskov and St\"ogbauer and Grassberger (KSG) in 2004, and is nonparametric and
based on the distances of each sample to its nearest neighboring
sample, where is a fixed small integer. Despite its widespread use (part of
scientific software packages), theoretical properties of this estimator have
been largely unexplored. In this paper we demonstrate that the estimator is
consistent and also identify an upper bound on the rate of convergence of the
bias as a function of number of samples. We argue that the superior performance
benefits of the KSG estimator stems from a curious "correlation boosting"
effect and build on this intuition to modify the KSG estimator in novel ways to
construct a superior estimator. As a byproduct of our investigations, we obtain
nearly tight rates of convergence of the error of the well known fixed
nearest neighbor estimator of differential entropy by Kozachenko and
Leonenko.Comment: 55 pages, 8 figure
Weighted k-Nearest-Neighbor Techniques and Ordinal Classification
In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into account. In this sense there is a close connection to LOESS, a local regression technique. In addition we show possibilities to use nearest neighbor for classification in the case of an ordinal class structure. Empirical studies show the advantages of the new techniques
- …