arXiv subjects

Wuzhou Zhang

Publications and source records attributed to Wuzhou Zhang.

3 recordsLinked to original sources

arXiv2016

Nearest-Neighbor Searching Under Uncertainty II

Nearest-neighbor search, which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest-neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for - computing all points that are nearest neighbors of a query point with nonzero probability; and - estimating the probability of a point being the nearest neighbor of a query point, either exactly or within a specified additive error.

cs.CG↗

arXiv2014

On Top-$k$ Weighted SUM Aggregate Nearest and Farthest Neighbors in the $L_1$ Plane

In this paper, we study top-$k$ aggregate (or group) nearest neighbor queries using the weighted SUM operator under the $L_1$ metric in the plane. Given a set $P$ of $n$ points, for any query consisting of a set $Q$ of $m$ weighted points and an integer $k$, $ 1 \le k \le n$, the top-$k$ aggregate nearest neighbor query asks for the $k$ points of $P$ whose aggregate distances to $Q$ are the smallest, where the aggregate distance of each point $p$ of $P$ to $Q$ is the sum of the weighted distances from $p$ to all points of $Q$. We build an $O(n\log n\log\log n)$-size data structure in $O(n\log n \log\log n)$ time, such that each top-$k$ query can be answered in $O(m\log m+(k+m)\log^2 n)$ time. We also obtain other results with trade-off between preprocessing and query. Even for the special case where $k=1$, our results are better than the previously best method (in PODS 2012), which requires $O(n\log^2 n)$ preprocessing time, $O(n\log^2 n)$ space, and $O(m^2\log^3 n)$ query time. In addition, for the one-dimensional version of this problem, our approach can build an $O(n)$-size data structure in $O(n\log n)$ time that can support $O(\min\{k,\log m\}\cdot m+k+\log n)$ time queries. Further, we extend our techniques to the top-$k$ aggregate farthest neighbor queries, with the same bounds.

cs.CG↗

arXiv2014

Convex Hulls under Uncertainty

We study the convex-hull problem in a probabilistic setting, motivated by the need to handle data uncertainty inherent in many applications, including sensor databases, location-based services and computer vision. In our framework, the uncertainty of each input site is described by a probability distribution over a finite number of possible locations including a \emph{null} location to account for non-existence of the point. Our results include both exact and approximation algorithms for computing the probability of a query point lying inside the convex hull of the input, time-space tradeoffs for the membership queries, a connection between Tukey depth and membership queries, as well as a new notion of $\some$-hull that may be a useful representation of uncertain hulls.

cs.CG↗