Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Multi-dimensional selectivity estimation using compressed histogram information
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Indexing the edges—a simple and yet efficient approach to high-dimensional indexing
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A comparison of DFT and DWT based similarity search in time-series databases
Proceedings of the ninth international conference on Information and knowledge management
ACM Computing Surveys (CSUR)
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
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
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Locality condensation: a new dimensionality reduction method for image retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
High-dimensional indexing with oriented cluster representation for multimedia databases
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Speed up kernel discriminant analysis
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 14th International Conference on Extending Database Technology
Effective data co-reduction for multimedia similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Efficient histogram-based similarity search in ultra-high dimensional space
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Self-taught dimensionality reduction on the high-dimensional small-sized data
Pattern Recognition
Data centric research at the University of Queensland
ACM SIGMOD Record
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The notorious “dimensionality curse” is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently.