The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Locally lifting the curse of dimensionality for nearest neighbor search (extended abstract)
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Proceedings of the 17th International Conference on Data Engineering
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Similarity Search without Tears: The OMNI Family of All-purpose Access Methods
Proceedings of the 17th International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Content-Based Image Retrieval Using Quasi-Gabor Filter and Reduction of Image Feature Dimension
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
IEEE Transactions on Knowledge and Data Engineering
Global Warp Metric Distance: Boosting Content-based Image Retrieval through Histograms
ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
Proceedings of the 2008 ACM symposium on Applied computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
Queries over sets of complex elements are performed extracting features from each element, which are used in place of the real ones during the processing. Extracting a large number of significant features increases the representative power of the feature vector and improves the query precision. However, each feature is a dimension in the representation space, consequently handling more features worsen the dimensionality curse. The problem derives from the fact that the elements tends to distribute all over the space and a large dimensionality allows them to spread over much broader spaces. Therefore, in high-dimensional spaces, elements are frequently farther from each other, so the distance differences among pairs of elements tends to homogenize. When searching for nearest neighbors, the first one is usually not close, but as long as one is found, small increases in the query radius tend to include several others. This effect increases the overlap between nodes in access methods indexing the dataset. Both spatial and metric access methods are sensitive to the problem. This paper presents a general strategy applicable to metric access methods in general, improving the performance of similarity queries in high dimensional spaces. Our technique applies a function that "stretches" the distances. Thus, close objects become closer and far ones become even farther. Experiments using the metric access method Slim-tree show that similarity queries performed in the transformed spaces demands up to 70% less distance calculations, 52% less disk access and reduces up to 57% in total time when comparing with the original spaces.