http://robowiki.net/w/index.php?action=history&feed=atom&title=Thread%3ATalk%3ADrussGT%2FUnderstanding_DrussGT%2FReason_behind_using_Manhattan_distance%2Freply_%288%29Thread:Talk:DrussGT/Understanding DrussGT/Reason behind using Manhattan distance/reply (8) - Revision history2026-04-11T05:10:52ZRevision history for this page on the wikiMediaWiki 1.34.1http://robowiki.net/w/index.php?title=Thread:Talk:DrussGT/Understanding_DrussGT/Reason_behind_using_Manhattan_distance/reply_(8)&diff=54781&oldid=prevSkilgannon: Reply to Reason behind using Manhattan distance2018-08-28T17:15:27Z<p>Reply to <a href="/wiki/Thread:Talk:DrussGT/Understanding_DrussGT/Reason_behind_using_Manhattan_distance/reply_(4)" title="Thread:Talk:DrussGT/Understanding DrussGT/Reason behind using Manhattan distance/reply (4)">Reason behind using Manhattan distance</a></p>
<p><b>New page</b></p><div>I think it is due to the noise rejection. For me it is the ratio between how a small change in a lot of dimensions is weighted compared to a big change in a single dimension, as you demonstrated above. You can also think about it like the difference between L1 and L2 distance, how they would affect a minimization problem. L1 rejects large noises, and is the most robust you can get while still maintaining a convex search space. L2 has a gradient that gets larger the bigger the distance, so dimensions with more error are effectively weighted higher, and weighted higher than just proportional to the amount of error.</div>Skilgannon