Results 1 - 10 of 5,932

Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets

by Christos Faloutsos, King-Ip (David) Lin , 1995
"... A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in k-d space, using k feature-extraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly fine-tuned spatial access methods (SAMs), to answer several ..."
Abstract - Cited by 502 (22 self) - Add to MetaCart
is user-defined), such that the dis-similarities are preserved. There are two benefits from this mapping: ...

Features of similarity.

by Amos Tversky - Psychological Review , 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
Abstract - Cited by 1455 (2 self) - Add to MetaCart
of stimulus and response generalization in learning, it is employed to explain errors in memory and pattern recognition, and it is central to the analysis of connotative meaning. Similarity or dissimilarity data appear in di¤erent forms: ratings of pairs, sorting of objects, communality between associations

Globally Consistent Range Scan Alignment for Environment Mapping

by F. Lu, E. Milios - AUTONOMOUS ROBOTS , 1997
"... A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is alig ..."
Abstract - Cited by 531 (8 self) - Add to MetaCart
A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data

Distance metric learning, with application to clustering with sideinformation,”

by Eric P Xing , Andrew Y Ng , Michael I Jordan , Stuart Russell - in Advances in Neural Information Processing Systems 15, , 2002
"... Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for ..."
Abstract - Cited by 818 (13 self) - Add to MetaCart
Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may

Pairwise data clustering by deterministic annealing

by Thomas Hofmann, Joachim M. Buhmann - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... Partitioning a data set and extracting hidden structure from the data arises in different application areas of pattern recognition, speech and image processing. Pairwise data clustering is a combinatorial optimization method for data grouping which extracts hidden structure from proximity data. We d ..."
Abstract - Cited by 235 (26 self) - Add to MetaCart
describe a deterministic annealing approach to pairwise clustering which shares the robustness properties of maximum entropy inference. The resulting Gibbs probability distributions are estimated by mean-field approximation. A new structure-preserving algorithm to cluster dissimilarity data

Fusion, Propagation, and Structuring in Belief Networks

by Judea Pearl - ARTIFICIAL INTELLIGENCE , 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
Abstract - Cited by 484 (8 self) - Add to MetaCart
to represent the generic knowledge of a domain expert, and it turns into a computational architecture if the links are used not merely for storing factual knowledge but also for directing and activating the data flow in the computations which manipulate this knowledge. The first part of the paper deals

Mixed membership stochastic block models for relational data with application to protein-protein interactions

by Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing, Tommi Jaakkola - In Proceedings of the International Biometrics Society Annual Meeting , 2006
"... We develop a model for examining data that consists of pairwise measurements, for example, presence or absence of links between pairs of objects. Examples include protein interactions and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with p ..."
Abstract - Cited by 378 (52 self) - Add to MetaCart
We develop a model for examining data that consists of pairwise measurements, for example, presence or absence of links between pairs of objects. Examples include protein interactions and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data

A new method for non-parametric multivariate analysis of variance in ecology.

by Marti J Anderson - Austral Ecology, , 2001
"... Abstract Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, ..."
Abstract - Cited by 368 (4 self) - Add to MetaCart
Abstract Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues

Learning from Labeled and Unlabeled Data using Graph Mincuts

by Avrim Blum , Shuchi Chawla , 2001
"... Many application domains suffer from not having enough labeled training data for learning. However, large amounts of unlabeled examples can often be gathered cheaply. As a result, there has been a great deal of work in recent years on how unlabeled data can be used to aid classification. We consi ..."
Abstract - Cited by 334 (5 self) - Add to MetaCart
consider an algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data. Our algorithm

Svm-knn: Discriminative nearest neighbor classification for visual category recognition

by Hao Zhang, Alexander C. Berg, Michael Maire, Jitendra Malik - in CVPR , 2006
"... We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While n ..."
Abstract - Cited by 342 (10 self) - Add to MetaCart
nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances
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