Discrete-time signal processing
Discrete-time signal processing
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Improving text retrieval for the routing problem using latent semantic indexing
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Digital image processing
Efficient retrieval for browsing large image databases
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
S3: similarity search in CAD database systems
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Principles of multimedia database systems
Principles of multimedia database systems
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Dimensionality reduction for similarity searching in dynamic databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Active disks: programming model, algorithms and evaluation
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
The Asilomar report on database research
ACM SIGMOD Record
Dimensionality reduction and similarity computation by inner product approximations
Proceedings of the ninth international conference on Information and knowledge management
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Digital Coding of Waveforms: Principles and Applications to Speech and Video
Digital Coding of Waveforms: Principles and Applications to Speech and Video
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Approximate Nearest Neighbor Searching in Multimedia Databases
Proceedings of the 17th 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
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
How to Approximate the Inner-product: Fast Dynamic Algorithms for Euclidean Similarity
How to Approximate the Inner-product: Fast Dynamic Algorithms for Euclidean Similarity
High dimensional nearest neighbor searching
Information Systems
Generating High Dimensional Data and Query Sets
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Approximate similarity search in metric spaces using inverted files
Proceedings of the 3rd international conference on Scalable information systems
Multimedia Tools and Applications
An approach to content-based image retrieval based on the Lucene search engine library
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
An effective method for approximating the euclidean distance in high-dimensional space
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Hi-index | 0.00 |
High dimensional data sets are encountered in many modern database applications. The usual approach is to construct a summary of the data set through a lossy compression technique, and use this lower dimensional synopsis to provide fast, approximate answers to the queries. In this paper, we develop a novel dimensionality reduction technique based on partitioning the high dimensional vector space into orthogonal subspaces. First, we find a relation between the Euclidian distance of two n-dimensional vectors and the Euclidian distances of their projections on the orthogonal subspaces. Then, based on this relation we develop a method to approximate the Euclidian distance using novel inner product approximation. This process allows us to incorporate the shape information of the vectors to this approximation. While the inner product approximation is symmetric, i.e., captures only the magnitude information of the data, the proposed method takes both the magnitude and shape information of the original vectors into account through partitioning. In the experiments, we demonstrate the effectiveness of our technique by comparing it with commonly used methods.