Jumping in late -- If your goal is to find nearest the N neighbors of a given point, look up the K-D Tree algorithm --( I used it some decades ago... in my case the points were each of higher dimension -- around 9 or so.)
And if that is the application, and you want to go fast - consider using something other than the L-2 norm to set up the tree. L-1 should be much faster - sum of absolute values of component distances; no squaring or square roots -- "Manhattan norm" Once you have built the tree and retrieved a bunch of nearest neighbors, you can of course compute the L-2 distances over that much smaller set, if necessary... S From: "Dang, Christophe" <christophe.d...@sidel.com> To: "International users mailing list for Scilab." <users@lists.scilab.org>, Date: 08/29/2014 06:58 AM Subject: Re: [Scilab-users] Pairwise distance of a huge amount of points Sent by: "users" <users-boun...@lists.scilab.org> Just to close the subject: I tried to implement the algorithm with sparse matrices, and it is less efficient than scanning over one dimension: 7 times faster than the naive algorithm. If I generate the sparse matrix from a n*(n-1)/2 vector, it is even worse: less efficient than the naive algorithm (1.1 times longer), this only to gain a factor 2 on the amount of points that can be handeled. Conclusion: forget the sparse matrices for this application. -- Christophe Dang Ngoc Chan Mechanical calculation engineer ______________________________________________________________________ This e-mail may contain confidential and/or privileged information. If you are not the intended recipient (or have received this e-mail in error), please notify the sender immediately and destroy this e-mail. Any unauthorized copying, disclosure or distribution of the material in this e-mail is strictly forbidden. ______________________________________________________________________ _______________________________________________ users mailing list users@lists.scilab.org http://cp.mcafee.com/d/1jWVIe418SyNsQsITd79EVpKrKrsKDtCXUUSVteXaaapJOWtSrLL6T4S7bLzC3t2toMY5v3UAvD8mVsTYfyh-sxrBPrTSvLtx_HYCCyyyyqeuLsKyqerTVBdV4sMZORQr8EGTsjVkffGhBrwqrhdECXYCej79zANOoUTsS03fBiteFqh-Ae00U9GX33VkDa3JsrFYq6SWv6xsxlK5LE2zVkDjGmAvF3w09JZdNcS2_id41Fr6bifQwnrFYq6BQQg1rphciFVEwgBiuMJVEwGWq8dd41dIzVEwRmHs9Cy2HFEwmHa4W6T6jp-xPMhmc
_______________________________________________ users mailing list users@lists.scilab.org http://lists.scilab.org/mailman/listinfo/users