  Finding similar data in high-dimensional spaces is one of the important tasks
in multimedia applications. Approaches introduced to find exact searching
techniques often use tree-based index structures which are known to suffer from
the curse of the dimensionality problem that limits their performance.
Approximate searching techniques prefer performance over accuracy and they
return good enough results while achieving a better performance. Locality
Sensitive Hashing (LSH) is one of the most popular approximate nearest neighbor
search techniques for high-dimensional spaces. One of the most time-consuming
processes in LSH is to find the neighboring points in the projected spaces. An
improved LSH-based index structure, called radius-optimized Locality Sensitive
Hashing (roLSH) has been proposed to utilize Machine Learning and efficiently
find these neighboring points; thus, further improve the overall performance of
LSH. In this paper, we extend roLSH by experimentally studying the effect of
different types of famous Machine Learning techniques on overall performance.
We compare ten regression techniques on four real-world datasets and show that
Neural Network-based techniques are the best fit to be used in roLSH as their
accuracy and performance trade-off are the best compared to the other
techniques.
