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
ACM Computing Surveys (CSUR)
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
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
VQ-index: an index structure for similarity searching in multimedia databases
Proceedings of the tenth ACM international conference on Multimedia
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
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
High-dimensional computational geometry
High-dimensional computational geometry
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
LDC: Enabling Search By Partial Distance In A Hyper-Dimensional Space
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
Information preserving XML schema embedding
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Global distance-based segmentation of trajectories
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Putting context into schema matching
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
ACM Transactions on Database Systems (TODS)
Unified framework for fast exact and approximate search in dissimilarity spaces
ACM Transactions on Database Systems (TODS)
BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information preserving XML schema embedding
ACM Transactions on Database Systems (TODS)
Approximate embedding-based subsequence matching of time series
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
NM-Tree: Flexible Approximate Similarity Search in Metric and Non-metric Spaces
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Towards faster activity search using embedding-based subsequence matching
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Learning Robust Similarity Measures for 3D Partial Shape Retrieval
International Journal of Computer Vision
Finding the Nearest Neighbors in Biological Databases Using Less Distance Computations
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On nonmetric similarity search problems in complex domains
ACM Computing Surveys (CSUR)
Embedding-based subsequence matching in time-series databases
ACM Transactions on Database Systems (TODS)
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A common problem in many types of databases is retrieving the most similar matches to a query object. Finding those 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. This paper proposes a novel method for approximate nearest neighbor retrieval in such spaces. Our method is embedding-based, meaning that it constructs a function that maps objects into a real vector space. The mapping preserves a large amount of the proximity structure of the original space, and it can be used to rapidly obtain a short list of likely matches to the query. The main novelty of our method is that it constructs, together with the embedding, a query-sensitive distance measure that should be used when measuring distances in the vector space. The term "query-sensitive" means that the distance measure changes depending on the current query object. We report experiments with an image database of handwritten digits, and a time-series database. In both cases, the proposed method outperforms existing state-of-the-art embedding methods, meaning that it provides significantly better trade-offs between efficiency and retrieval accuracy.