The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
H-Map: A Dimension Reduction Mapping for Approximate Retrieval of Multi-dimensional Data
DS '99 Proceedings of the Second International Conference on Discovery Science
A Tree Distance Function Based on Multi-sets
New Frontiers in Applied Data Mining
Efficient Similarity Search by Reducing I/O with Compressed Sketches
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
On the configuration of the similarity search data structure d-index for high dimensional objects
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Accelerating video identification by skipping queries with a compact metric cache
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
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Approximate retrieval of multi-dimensional data, such as documents, digital images, and audio clips, is a method to get objects within some dissimilarity from a given object. We assume a metric space containing objects, where distance is used to measure dissimilarity. In Euclidean metric spaces, approximate retrieval is easily and efficiently realized by a spatial indexing/access method R-tree. First, we consider objects in discrete L1 (or Manhattan distance) metric space, and present embedding method into Euclidean space for them. Then, we propose a projection mapping H-Map to reduce dimensionality of multi-dimensional data, which can be applied to any metric space such as L1 or L驴 metric space, as well as Euclidean space. H-Map does not require coordinates of data unlike K-L transformation. H-Map has an advantage in using spatial indexing such as R-tree because it is a continuous mapping from a metric space to an L驴 metric space, where a hyper-sphere is a hyper-cube in the usual sense. Finally we show that the distance function itself, which is simpler than H-Map, can be used as a dimension reduction mapping for any metric space.