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Learning to Prune in Metric and NonMetric Spaces
"... Our focus is on approximate nearest neighbor retrieval in metric and nonmetric spaces. We employ a VPtree and explore two simple yet effective learningtoprune approaches: density estimation through sampling and “stretching ” of the triangle inequality. Both methods are evaluated using data sets ..."
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Our focus is on approximate nearest neighbor retrieval in metric and nonmetric spaces. We employ a VPtree and explore two simple yet effective learningtoprune approaches: density estimation through sampling and “stretching ” of the triangle inequality. Both methods are evaluated using data sets
CPIndex: Using Clustering and Pivots for Indexing NonMetric Spaces ABSTRACT
"... Most multimedia information retrieval systems use an indexing scheme to speed up similarity search. The index aims to discard large portions of the data collection at query time. Generally, these approaches use the triangular inequality to discard elements or groups of elements, thus requiring that ..."
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to use the traditional approaches for indexing. In this paper we introduce the CPindex, a new approximate indexing technique for nonmetric spaces that combines clustering and pivots. The index dynamically adapts to the conditions of the nonmetric space using pivots when the fraction of triplets
Robust Scalable Visualized Clustering in Metric and nonMetric Spaces
"... We describe an approach to data analytics on large systems using a suite of robust parallel algorithms running on both clouds and HPC systems. We apply this to cases where the data is defined in a vector space and when only pairwise distances between points are defined. We introduce improvements to ..."
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to known algorithms for functionality, features and performance. Visualization is valuable for steering complex analytics and we discuss it for both the nonmetric case and for clustering high dimension vector spaces. We exploit deterministic annealing which is heuristic but has clear general principles
DynDex: A Dynamic and Nonmetric Space Indexer
, 2002
"... To date, almost all research work in the ContentBased Image Retrieval (CBIR) community has used Minkowskilike functions to measure similarity between images. In this paper, we first present a nonmetric distance function, dynamic partial function (DPF), which works significantly better than Minkow ..."
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Cited by 17 (5 self)
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To date, almost all research work in the ContentBased Image Retrieval (CBIR) community has used Minkowskilike functions to measure similarity between images. In this paper, we first present a nonmetric distance function, dynamic partial function (DPF), which works significantly better than
ISBN 9788073780654 © MATFYZPRESS On Mtree Variants in Metric and Nonmetric Spaces
"... Abstract. Although there have been many metric access methods (MAMs) developed so far to solve the problem of similarity searching, there is still big need for gapping retrieval efficiency. One of the most acceptable MAMs is Mtree which meets the essential features important for large, persistent a ..."
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our new reinserting algorithm which is dynamic with acceptable construction costs and which reorganizes efficiently index. Another approach how to improve similarity searching has been introduced with the TriGen algorithm [Skopal, 2006] which enables MAMs to perform also nonmetric similarity search
NonMetric ImageBased Rendering for Video Stabilization
"... We consider the problem of video stabilization: removing unwanted image perturbations due to unstable camera motions. We approach this problem from an imagebased rendering (IBR) standpoint. Given an unstabilized video sequence, the task is to synthesize a new sequence as seen from a stabilized came ..."
Mtree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 652 (38 self)
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A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion
Antide Sitter Space, Thermal Phase Transition, and Confinement in Gauge Theories
 Adv. Theor. Math. Phys
, 1998
"... The correspondence between supergravity (and string theory) on AdS space and boundary conformal field theory relates the thermodynamics of N = 4 super YangMills theory in four dimensions to the thermodynamics of Schwarzschild black holes in Antide Sitter space. In this description, quantum phenome ..."
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Cited by 1087 (4 self)
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The correspondence between supergravity (and string theory) on AdS space and boundary conformal field theory relates the thermodynamics of N = 4 super YangMills theory in four dimensions to the thermodynamics of Schwarzschild black holes in Antide Sitter space. In this description, quantum
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edg ..."
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Cited by 1276 (124 self)
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A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)
Gradient flows in metric spaces and in the space of probability measures
 LECTURES IN MATHEMATICS ETH ZÜRICH, BIRKHÄUSER VERLAG
, 2005
"... ..."
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