Results 1  10
of
374,063
Fibonacci Heaps and Their Uses in Improved Network optimization algorithms
, 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated Fheaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. Fheaps support arbitrary deletion from an nitem heap in qlogn) amortized tim ..."
Abstract

Cited by 739 (18 self)
 Add to MetaCart
in the problem graph: ( 1) O(n log n + m) for the singlesource shortest path problem with nonnegative edge lengths, improved from O(m logfmh+2)n); (2) O(n*log n + nm) for the allpairs shortest path problem, improved from O(nm lo&,,,+2,n); (3) O(n*logn + nm) for the assignment problem (weighted bipartite
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
Abstract

Cited by 676 (15 self)
 Add to MetaCart
of the leaves were represented by a noisyor: ? (Child= OIParents) = eBoL; B,Paren t; where 110 represents the "leak" term. The QMRDT network The QMRDT is a bipartite network whose structure is the same as that shown in figure 2 but the size is much larger. There are approximately 600 diseases
Loopy belief propagation for bipartite maximum weight bmatching
 in Artificial Intelligence and Statistics (AISTATS
, 2007
"... We formulate the weighted bmatching objective function as a probability distribution function and prove that belief propagation (BP) on its graphical model converges to the optimum. Standard BP on our graphical model cannot be computed in polynomial time, but we introduce an algebraic method to cir ..."
Abstract

Cited by 51 (13 self)
 Add to MetaCart
We formulate the weighted bmatching objective function as a probability distribution function and prove that belief propagation (BP) on its graphical model converges to the optimum. Standard BP on our graphical model cannot be computed in polynomial time, but we introduce an algebraic method
Adaptive Anonymity via bMatching
"... The adaptive anonymity problem is formalized where each individual shares their data along with an integer value to indicate their personal level of desired privacy. This problem leads to a generalization of kanonymity to the bmatching setting. Novel algorithms and theory are provided to implement ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
The adaptive anonymity problem is formalized where each individual shares their data along with an integer value to indicate their personal level of desired privacy. This problem leads to a generalization of kanonymity to the bmatching setting. Novel algorithms and theory are provided
BMatching for Spectral Clustering
"... Abstract. We propose preprocessing spectral clustering with bmatching to remove spurious edges in the adjacency graph prior to clustering. Bmatching is a generalization of traditional maximum weight matching and is solvable in polynomial time. Instead of a permutation matrix, it produces a binary ..."
Abstract

Cited by 20 (4 self)
 Add to MetaCart
Abstract. We propose preprocessing spectral clustering with bmatching to remove spurious edges in the adjacency graph prior to clustering. Bmatching is a generalization of traditional maximum weight matching and is solvable in polynomial time. Instead of a permutation matrix, it produces a binary
Restricted bmatchings in degreebounded graphs
, 2009
"... We present a minmax formula and a polynomial time algorithm for a slight generalization of the following problem: in a simple undirected graph in which the degree of each node is at most t+1, find a maximum tmatching containing no member of a list K of forbidden Kt;t and Kt+1 subgraphs. An analogo ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
We present a minmax formula and a polynomial time algorithm for a slight generalization of the following problem: in a simple undirected graph in which the degree of each node is at most t+1, find a maximum tmatching containing no member of a list K of forbidden Kt;t and Kt+1 subgraphs
Approximation Algorithms for Connected Dominating Sets
 Algorithmica
, 1996
"... The dominating set problem in graphs asks for a minimum size subset of vertices with the following property: each vertex is required to either be in the dominating set, or adjacent to some node in the dominating set. We focus on the question of finding a connected dominating set of minimum size, whe ..."
Abstract

Cited by 366 (9 self)
 Add to MetaCart
, where the graph induced by vertices in the dominating set is required to be connected as well. This problem arises in network testing, as well as in wireless communication. Two polynomial time algorithms that achieve approximation factors of O(H (\Delta)) are presented, where \Delta is the maximum
BMatching for Embedding
"... When learning from a dataset of samples, many algorithms begin by forming a graph that captures pairwise affinities between all pairs of points. For example, spectral clustering forms a weighted graph between datapoints and then approximates a normalized cut on this graph [4, 2]. Nonlinear manifold ..."
Abstract
 Add to MetaCart
step yet might be suboptimal: either keeping the graph fully connected or greedily building a sparse graph via, for example, knearestneighbor. We propose using bmatching [3], an interesting polynomialtime and optimal algorithm for finding the maximum weight subgraph from a larger graph
Results 1  10
of
374,063