e \(l^2\) or \(l^1\) norm is considered.</li>
</ol>
<p>The key is to trade-off carefully the speedup obtained by using the ANN algorithm and the loss in accuracy when retrieving neighbors. Due to the curse of dimensionality, KD-trees become less effective for higher dimensional data, so that the search cost, which in the best case is logarithmic with this data structure, may become effectively linear. This is somehow mitigated by the fact that new a new KD-tree is computed at each iteration, reducing the li