Editing Talk:1781: Artifacts
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: Though this is really fascinating idea, I think that it is not completely correct. You would need to define outliers in each dimension separately. If you's use n-dimensional distance, the points will be all roughly equidistant from the mean. --[[Special:Contributions/162.158.134.106|162.158.134.106]] 10:42, 5 January 2017 (UTC) | : Though this is really fascinating idea, I think that it is not completely correct. You would need to define outliers in each dimension separately. If you's use n-dimensional distance, the points will be all roughly equidistant from the mean. --[[Special:Contributions/162.158.134.106|162.158.134.106]] 10:42, 5 January 2017 (UTC) | ||
: I think therefore that "One way to have a data set composed entirely of outliers would be a data set with N points, in an N-dimentional space, where each point is zero for every dimension except one, unique to itself.[http://math.stackexchange.com/questions/1302395/n-points-can-be-equidistant-from-each-other-only-in-dimensions-ge-n-1] All these points are equidistant from each other." should be removed from the text. In an equidistant data set, no point is an outlier.--[[Special:Contributions/162.158.134.106|162.158.134.106]] 10:50, 5 January 2017 (UTC) | : I think therefore that "One way to have a data set composed entirely of outliers would be a data set with N points, in an N-dimentional space, where each point is zero for every dimension except one, unique to itself.[http://math.stackexchange.com/questions/1302395/n-points-can-be-equidistant-from-each-other-only-in-dimensions-ge-n-1] All these points are equidistant from each other." should be removed from the text. In an equidistant data set, no point is an outlier.--[[Special:Contributions/162.158.134.106|162.158.134.106]] 10:50, 5 January 2017 (UTC) | ||
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