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so letamp;#39;s understand two concepts because here are T Steen we have stochastic neighborhood embedding so let us understand the last two terms neighborhood and everybody what do they mean imagine in my D dimensional space itamp;#39;s assumed this is my D dimensional space okay letamp;#39;s assume this is my D dimensional space of course I canamp;#39;t draw any dimensional space that is more than two but letamp;#39;s just assume that this is my D dimensional space Iamp;#39;ll just put points here without writing axis okay suppose if I have letamp;#39;s say if I have points like this okay suppose if I have points like this right suppose if I have my points like this in my D dimensional space in a high dimensional space or letamp;#39;s also ensure that D is large enough so letamp;#39;s say you mean my high dimensional space letamp;#39;s say you my data is like this so if you take RM Minister data set okay we have 784 dimensions and letamp;#39;s assume all our zeroes are lik