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126,026
Fast neighborhood graph search using cartesian concatenation
 In ICCV
"... In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of subvectors ..."
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Cited by 3 (3 self)
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In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets
Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method
, 904
"... Neighborhood graphs are gaining popularity as a concise data representation in machine learning. However, naive graph construction by pairwise distance calculation takes O(n 2) runtime for n data points and this is prohibitively slow for millions of data points. For strings of equal length, the mult ..."
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Cited by 1 (0 self)
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Neighborhood graphs are gaining popularity as a concise data representation in machine learning. However, naive graph construction by pairwise distance calculation takes O(n 2) runtime for n data points and this is prohibitively slow for millions of data points. For strings of equal length
On Triangulationbased Dense Neighborhood Graph Discovery
"... This paper introduces a new definition of dense subgraph pattern, the DNgraph. DNgraph considers both the size of the substructure and the minimum level of interactions between any pair of the vertices. The mining of DNgraphs inherits the difficulty of finding clique, the fullyconnected subgraph ..."
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Cited by 18 (1 self)
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This paper introduces a new definition of dense subgraph pattern, the DNgraph. DNgraph considers both the size of the substructure and the minimum level of interactions between any pair of the vertices. The mining of DNgraphs inherits the difficulty of finding clique, the fully
High dimensional graphs and variable selection with the Lasso
 ANNALS OF STATISTICS
, 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
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Cited by 733 (22 self)
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is a computationally attractive alternative to standard covariance selection for sparse highdimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for each node in the graph and is hence equivalent to variable selection for Gaussian linear models. We
Efficient graphbased image segmentation.
 International Journal of Computer Vision,
, 2004
"... Abstract. This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graphbased representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show ..."
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Cited by 942 (1 self)
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that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm
The pCompetition Graphs of Symmetric Digraphs and pNeighborhood Graphs
"... . The pcompetition graph G of a digraph D is a graph on the same vertex set as D, with [x; y] 2 E(G) if and only if jOut(x) " Out(y)j p in D. In this paper we focus on the case in which D is a symmetric digraph ((a; b) is an arc in D if and only if (b; a) is an arc in D). We relate the prob ..."
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the problem to 2step graphs, squares, and a generalization of the neighborhood graph called the pneighborhood graph. We also identify some familiar classes of graphs as 2competition graphs of loopless symmetric digraphs. I. Introduction. The pcompetition graph was introduced in 1989 by Kim, McKee, Mc
Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement
 PROCEEDINGS OF THE TWENTIETH INTERNATIONAL CONFERENCE ON AUTOMATED PLANNING AND SCHEDULING (ICAPS 2010)
, 2010
"... Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC2008 used the cost of the best known plan divided by the c ..."
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Cited by 15 (9 self)
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by the cost of the generated plan as an evaluation metric. This paper proposes and evaluates two simple but effective methods for plan improvement: Action Elimination improves an existing plan by repeatedly removing sets of irrelevant actions. Plan Neighborhood Graph Search finds a new, shorter plan
Neighborhood Graphs, Stripes and Shadow Plots for Cluster Visualization
"... This is a preprint of an article that has been accepted for publication in ..."
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This is a preprint of an article that has been accepted for publication in
Less is more: sampling the neighborhood graph makes salsa better and faster
 In WSDM
, 2009
"... In this paper, we attempt to improve the effectiveness and the efficiency of querydependent linkbased ranking algorithms such as HITS, MAX and SALSA. All these ranking algorithms view the results of a query as nodes in the web graph, expand the result set to include neighboring nodes, and compute ..."
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Cited by 20 (5 self)
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, and compute scores on the induced neighborhood graph. In previous work it was shown that SALSA in particular is substantially more effective than queryindependent linkbased ranking algorithms such as PageRank. In this work, we show that whittling down the neighborhood graph through consistent sampling
SemiAutomated Derivation of Conceptual Neighborhood Graphs of Topological Relations
"... Abstract. Conceptual neighborhood graphs are similaritybased schemata of spatial/temporal relations. This paper proposes a semiautomated method for deriving a conceptual neighborhood graph of topological relations, which shows all pairs of relations between which a smooth transformation can be pe ..."
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Cited by 1 (1 self)
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Abstract. Conceptual neighborhood graphs are similaritybased schemata of spatial/temporal relations. This paper proposes a semiautomated method for deriving a conceptual neighborhood graph of topological relations, which shows all pairs of relations between which a smooth transformation can
Results 21  30
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126,026