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130,188
Building Connected Neighborhood Graphs for Isometric Data Embedding
, 2005
"... Neighborhood graph construction is usually the first step in algorithms for isometric data embedding and manifold learning that cope with the problem of projecting high dimensional data to a low space. This paper begins by explaining the algorithmic fundamentals of techniques for isometric data embe ..."
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Cited by 1 (1 self)
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Neighborhood graph construction is usually the first step in algorithms for isometric data embedding and manifold learning that cope with the problem of projecting high dimensional data to a low space. This paper begins by explaining the algorithmic fundamentals of techniques for isometric data
Graph laplacians and their convergence on random neighborhood graphs
 Journal of Machine Learning Research
, 2006
"... Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semisupervised learning, d ..."
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Cited by 35 (7 self)
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Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semisupervised learning
Conceptual Neighborhood Graphs for Topological Spatial Relations
"... Abstract—This paper presents the conceptual neighborhood graphs with the transitions between the topological spatial relations that can exist between a circular spatially extended point and a line. The final objective of this work is the use of the transitions in the prediction of a mobile user posi ..."
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Abstract—This paper presents the conceptual neighborhood graphs with the transitions between the topological spatial relations that can exist between a circular spatially extended point and a line. The final objective of this work is the use of the transitions in the prediction of a mobile user
Bipartite and neighborhood graphs and the spectrum of the normalized graph
 Laplacian, Comm. Anal. Geom
"... We study the spectrum of the normalized Laplace operator of a connected graph Γ. As is well known, the smallest nontrivial eigenvalue measures how difficult it is to decompose Γ into two large pieces, whereas the largest eigenvalue controls how close Γ is to being bipartite. The smallest eigenvalu ..."
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Cited by 9 (5 self)
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eigenvalue can be controlled by the Cheeger constant, and we establish a dual construction that controls the largest eigenvalue. Moreover, we find that the neighborhood graphs Γ[l] of order l ≥ 2 encode important spectral information about Γ itself which we systematically explore. In particular, we can
Temporalizing Spatial Calculi: On Generalized Neighborhood Graphs
"... Abstract. To reason about geographical objects, it is not only necessary to have more or less complete information about where these objects are located in space, but also how they can change their position, shape, and size over time. In this paper we investigate how calculi discussed in the field o ..."
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can be expressed in such languages and how continuous change is represented in the socalled conceptual neighborhood graph of the spatial calculus at hand. In a second step, we focus on a special reasoning problem, which occurs quite naturally in the context of temporalized spatial calculi: Given
Visualizing Gene Clusters using Neighborhood Graphs in R
"... Abstract. The visualization of cluster solutions in gene expression data analysis gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Neighborhood graphs allow for visual assessment of relationships between adjacent cluste ..."
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Cited by 4 (4 self)
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Abstract. The visualization of cluster solutions in gene expression data analysis gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Neighborhood graphs allow for visual assessment of relationships between adjacent
Navigating the semantic horizon using relative neighborhood graph
 In Proceedings of EMNLP
, 2015
"... This paper introduces a novel way to navigate neighborhoods in distributional semantic models. The approach is based on relative neighborhood graphs, which uncover the topological structure of local neighborhoods in semantic space. This has the potential to overcome both the problem with selecting ..."
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Cited by 2 (1 self)
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This paper introduces a novel way to navigate neighborhoods in distributional semantic models. The approach is based on relative neighborhood graphs, which uncover the topological structure of local neighborhoods in semantic space. This has the potential to overcome both the problem
Estimation of Tangent Planes for Neighborhood Graph Correction
"... Abstract. Local algorithms for nonlinear dimensionality reduction [1], [2], [3], [4], [5] and semisupervised learning algorithms [6], [7] use spectral decomposition based on a nearest neighborhood graph. In the presence of shortcuts (union of two points whose distance measure along the submanifold ..."
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Cited by 1 (0 self)
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Abstract. Local algorithms for nonlinear dimensionality reduction [1], [2], [3], [4], [5] and semisupervised learning algorithms [6], [7] use spectral decomposition based on a nearest neighborhood graph. In the presence of shortcuts (union of two points whose distance measure along
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
Results 11  20
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130,188