Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Multidimensional access methods
ACM Computing Surveys (CSUR)
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Mixture of KL subspaces for relevance feedback
Multimedia Tools and Applications
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Recently, multidimensional point indexing has generated a great deal of interest in applications where objects are usually represented through feature vectors belonging to high-dimensional spaces and are searched by similarity according to a given example. Unfortunately, although traditional data structures and access methods work well for low-dimensional spaces, they perform poorly as dimensionality increases. The application of a dimensionality reduction approach, such as the Karhunen-Lo猫ve transform, is often not very effecttive to deal with the indexing problem, since the substantial loss of information does not allow patterns to be sufficiently discriminated in the reduced space. In this work we present a novel hierarchical data structure based on the Multispace KL transform, a generalization of the KL transform, specifically designed to cope with locally correlated data. In the MKL-tree, dimensionality reduction is performed at each node, allowing more selective features to be extracted and thus increasing the discriminant power of the index. In this work the mathematical foundations and the algorithms on which the MKL-tree is based are presented and preliminary experimental results are reported.