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--- ece4580:module_pcd:localneighborhood [2017/02/21 13:00] – pvela
+++ ece4580:module_pcd:localneighborhood [2024/08/20 21:38] (current) – external edit 127.0.0.1
@@ Line 48: / Line 48: @@
 Radius selection for locality calculations has been studied a fair amount, with many folk coming up with different rules.  Surely trying to figure out what this number should be by guess and checking is probably not optimal, and definitely does not lend itself to automation.  Here are a couple of ideas:
-  - Compute the median distance.  For that, the ''pdist'' function could be useful.
+  - Compute the median distance of all pair-wise point distances.  For that, the ''pdist'' function could be useful.
   - Compute the average distance of only the $k$-nearest neighbors of a point set to itself. Basically, the $k$-nearest neighbors are sought for each point, then all of the distances are collected for these neighbors.   Their mean is then computed (or median if you'd prefer). No for loops should be necessary. Matlab's ''knnsearch'' method does most of the work.  A couple lines later, you are good to go. \\ The value $k$ should be chosen so that you almost always get the number of neighbors you need with the radius chosen. Say you need 10 points, then a good value for $k$ would be something like 15. It should be that more than 70% of the time, you will get 10 points.