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2,222
Document Versioning Using Feature Space Distances
"... Abstract. The automated analysis of documents is an important task given the rapid increase in availability of digital texts. In an earlier pub-lication, we had presented a framework where the edit distances be-tween documents was used to reconstruct the version history of a set of documents. Howeve ..."
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Abstract. The automated analysis of documents is an important task given the rapid increase in availability of digital texts. In an earlier pub-lication, we had presented a framework where the edit distances be-tween documents was used to reconstruct the version history of a set of documents
Adaptive feature-space conformal transformation for imbalanced-data learning
- In Proc. ICML
, 2003
"... When the training instances of the target class are heavily outnumbered by non-target training instances, SVMs can be ineffective in determin-ing the class boundary. To remedy this problem, we propose an adaptive conformal transformation (ACT) algorithm. ACT considers feature-space distance and the ..."
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Cited by 29 (9 self)
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When the training instances of the target class are heavily outnumbered by non-target training instances, SVMs can be ineffective in determin-ing the class boundary. To remedy this problem, we propose an adaptive conformal transformation (ACT) algorithm. ACT considers feature-space distance
Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
, 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
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Cited by 502 (22 self)
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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
Features of similarity.
- 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 ..."
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Cited by 1455 (2 self)
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. These models represent objects as points in some coordinate space such that the observed dissimilarities between objects correspond to the metric distances between the respective points. Practically all analyses of proximity data have been metric in nature, although some (e.g., hierarchical clustering) yield
Approximate distance oracles
, 2004
"... Let G = (V, E) be an undirected weighted graph with |V | = n and |E | = m. Let k ≥ 1 be an integer. We show that G = (V, E) can be preprocessed in O(kmn 1/k) expected time, constructing a data structure of size O(kn 1+1/k), such that any subsequent distance query can be answered, approximately, in ..."
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Cited by 273 (9 self)
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, in O(k) time. The approximate distance returned is of stretch at most 2k − 1, i.e., the quotient obtained by dividing the estimated distance by the actual distance lies between 1 and 2k−1. A 1963 girth conjecture of Erdős, implies that Ω(n 1+1/k) space is needed in the worst case for any real stretch
Blobworld: A System for Region-Based Image Indexing and Retrieval
- In Third International Conference on Visual Information Systems
, 1999
"... . Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Querying is based on the attributes of one or two regions of ..."
Abstract
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Cited by 375 (4 self)
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of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions using a tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
- Machine Learning
, 1993
"... In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of t ..."
Abstract
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Cited by 309 (3 self)
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of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature
Greedy spectral embedding
"... Spectral dimensionality reduction methods and spectral clustering methods require computation of the principal eigenvectors of an n × n matrix where n is the number of examples. Following up on previously proposed techniques to speed-up kernel methods by focusing on a subset of m examples, we study ..."
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Cited by 20 (2 self)
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a greedy selection procedure for this subset, based on the feature-space distance between a candidate example and the span of the previously chosen ones. In the case of kernel PCA or spectral clustering this reduces computation to O(m² n). For the same computational complexity, we can also compute
Region Covariance: A Fast Descriptor for Detection And Classification
- In Proc. 9th European Conf. on Computer Vision
, 2006
"... We describe a new region descriptor and apply it to two problems, object detection and texture classification. The covariance of d-features, e.g., the three-dimensional color vector, the norm of first and second derivatives of intensity with respect to x and y, etc., characterizes a region of in ..."
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Cited by 278 (14 self)
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. Covariance matrices do not lie on Euclidean space, therefore we use a distance metric involving generalized eigenvalues which also follows from the Lie group structure of positive definite matrices. Feature matching is a simple nearest neighbor search under the distance metric and performed extremely
Feature Selection via Concave Minimization and Support Vector Machines
- Machine Learning Proceedings of the Fifteenth International Conference(ICML ’98
, 1998
"... Computational comparison is made between two feature selection approaches for finding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave minimization approach [19, 5] a separat ..."
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Cited by 263 (23 self)
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Computational comparison is made between two feature selection approaches for finding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave minimization approach [19, 5] a
Results 1 - 10
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2,222
