Advanced Algorithms and Data Structures by Marcello La Rocca

Author:Marcello La Rocca [Rocca, Marcello La]
Language: eng
Format: epub, pdf
Publisher: Manning Publications Co.
Published: 0101-01-01T00:00:00+00:00

10.4.3 Approximated similarity search

As mentioned, similarity search in k-d trees, as well as R-trees and SS-trees, suffers from what is called the curse of dimensionality: the methods on these data structures become exponentially slower with the growth of the number of dimensions of the search space. K-d trees also suffer from additional sparsity issues that become more relevant in higher dimensions.

While using R-trees and SS-trees can improve the balance of the trees and result in better trees and faster construction, there is still something more we can do to improve the performance of the similarity search methods.

These approximate search methods are indeed a tradeoff between accuracy and performance; there are a few different (and sometimes complementary) strategies that can be used to have faster approximate queries:

Reduce the dimensionality of the objects—Using algorithms such as PCA or Discrete Fourier Transform, it is possible to project the dataset’s object into a different, lower-dimensional space. The idea is that this space will maintain only the essential information to distinguish between different points in the dataset. With dynamic datasets, this method is obviously less effective.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.