Random Projection Trees Revisited

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Random Projection Trees Revisited
The Random Projection Tree (RPTREE) structures proposed in [1] are space partitioning data structures that automatically adapt to various notions of intrinsic dimensionality of data. We prove new results for both the RPTREE-MAX and the RPTREE-MEAN data structures. Our result for RPTREE-MAX gives a nearoptimal bound on the number of levels required by this data structure to reduce the size of its cells by a factor s 2. We also prove a packing lemma for this data structure. Our final result shows that low-dimensional manifolds have bounded Local Covariance Dimension. As a consequence we show that RPTREE-MEAN adapts to manifold dimension as well.
Aman Dhesi, Purushottam Kar
Added 09 Dec 2010
Updated 29 Dec 2010
Type Journal
Year 2010
Where CORR
Authors Aman Dhesi, Purushottam Kar
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