{"comment":{"url":"https://api.github.com/repos/Smorodov/eos/comments/17598775","html_url":"https://github.com/Smorodov/eos/commit/e5e9b3f349f227c24d42b15284130a8e88829501#commitcomment-17598775","id":17598775,"user":{"login":"Smorodov","id":1816461,"avatar_url":"https://avatars.githubusercontent.com/u/1816461?v=3","gravatar_id":"","url":"https://api.github.com/users/Smorodov","html_url":"https://github.com/Smorodov","followers_url":"https://api.github.com/users/Smorodov/followers","following_url":"https://api.github.com/users/Smorodov/following{/other_user}","gists_url":"https://api.github.com/users/Smorodov/gists{/gist_id}","starred_url":"https://api.github.com/users/Smorodov/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Smorodov/subscriptions","organizations_url":"https://api.github.com/users/Smorodov/orgs","repos_url":"https://api.github.com/users/Smorodov/repos","events_url":"https://api.github.com/users/Smorodov/events{/privacy}","received_events_url":"https://api.github.com/users/Smorodov/received_events","type":"User","site_admin":false},"position":null,"line":null,"path":null,"commit_id":"e5e9b3f349f227c24d42b15284130a8e88829501","created_at":"2016-05-24T17:03:03Z","updated_at":"2016-05-24T17:03:03Z","body":"And I think there should be sparse matrices, and may be it will work faster\nwith using Eigen Sparse matrices ?\n\n2016-05-24 20:01 GMT+03:00 Андрей Смородов <andreysmorodov@gmail.com>:\n\n> As I understand, we will have normal distribution with any scaling, it\n> will differ with only covariance matrix and will be elliptic in general\n> case.\n> Orthonormalized,\n> I mean case when after scaling, covarince matrix become equal to\n> sigma*eye, i.e. round like (in 2 D case).\n> [image: Встроенное изображение 2]\n> And optimization is faster and more accurate in last case.\n>\n> 2016-05-24 19:14 GMT+03:00 Patrik Huber <notifications@github.com>:\n>\n>> Great, thanks for the reply and clarification!\n>>\n>> Actually, we usually fit with the \"normalised\" basis, the one that is\n>> \"premultiplied\" with the Eigenvalues, and not with the orthonormal\n>> (=\"unnormalised\") basis. If you fit with the \"normalised\" = \"premultiplied\"\n>> basis, then the resulting coefficients will be normal distributed. I think\n>> you are saying the opposite, aren't you?\n>>\n>> —\n>> You are receiving this because you authored the thread.\n>> Reply to this email directly or view it on GitHub\n>> <https://github.com/Smorodov/eos/commit/e5e9b3f349f227c24d42b15284130a8e88829501#commitcomment-17597933>\n>>\n>\n>\n"}}
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