SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality reduction for similarity searching in dynamic databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Dimensionality reduction and similarity computation by inner product approximations
Proceedings of the ninth international conference on Information and knowledge management
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class-dependent weighted dissimilarity measure for nearest neighbor classification problems
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Approximate similarity retrieval with M-trees
The VLDB Journal — The International Journal on Very Large Data Bases
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-preserving Embedding of Proteins
Cluster-preserving Embedding of Proteins
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
LDC: Enabling Search By Partial Distance In A Hyper-Dimensional Space
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning embeddings for indexing, retrieval, and classification, with applications to object and shape recognition in image databases
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nearest neighbor search methods for handshape recognition
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
A database-based framework for gesture recognition
Personal and Ubiquitous Computing
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A common problem in many types of databases is retrieving the most similar matches to a query object. Finding these matches in a large database can be too slow to be practical, especially in domains where objects are compared using computationally expensive similarity (or distance) measures. Embedding methods can significantly speed-up retrieval by mapping objects into a vector space, where distances can be measured rapidly using a Minkowski metric. In this article we present a novel way to improve embedding quality. In particular, we propose to construct embeddings that use a query-sensitive distance measure for the target space of the embedding. This distance measure is used to compare those vectors that the query and database objects are mapped to. The term “query-sensitive” means that the distance measure changes, depending on the current query object. We demonstrate theoretically that using a query-sensitive distance measure increases the modeling power of embeddings and allows them to capture more of the structure of the original space. We also demonstrate experimentally that query-sensitive embeddings can significantly improve retrieval performance. In experiments with an image database of handwritten digits and a time-series database, the proposed method outperforms existing state-of-the-art non-Euclidean indexing methods, meaning that it provides significantly better tradeoffs between efficiency and retrieval accuracy.