Sciweavers

Share
CORR
2010
Springer

Random Projection Trees Revisited

9 years 9 months ago
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
Comments (0)
books