how to fast compute distance between high dimension vectors
Posted
by chyojn
on Stack Overflow
See other posts from Stack Overflow
or by chyojn
Published on 2010-05-23T13:24:12Z
Indexed on
2010/05/23
13:30 UTC
Read the original article
Hit count: 174
algorithm
assume there are three group of high dimension vectors:
{a_1, a_2, ..., a_N},
{b_1, b_2, ... , b_N},
{c_1, c_2, ..., c_N}.
each of my vector can be represented as: x = a_i + b_j + c_k, where 1 <=i, j, k <= N. then the vector is encoded as (i, j, k) wich is then can be decoded as x = a_i + b_j + c_k.
my question is, if there are two vector: x = (i_1, j_1, k_1), y = (i_2, j_2, k_2), is there a method to compute the euclidian distance of these two vector without decode x and y.
© Stack Overflow or respective owner