Outlier를 제거하는 알고리즘 중 하나...
( RANSAC's Algorithm )
Given: data - a set of observed data points
model - a model that can be fitted to data points
n - the minimum number of data values required to fit the model
k - the maximum number of iterations allowed in the algorithm
t - a threshold value for determining when a data point fits a model
d - the number of close data values required to assert that a model fits well to data
Return: bestfit - model parameters which best fit the data (or nil if no good model is found)
iterations = 0
bestfit = nil
besterr = something really large
while iterations < k
maybeinliers = n, randomly selected values from data
maybemodel = model parameters fitted to maybeinliers
alsoinliers = empty set
for every point in data not in maybeinliers
if point fits maybemodel with an error smaller than t
add point to alsoinliers
if the number of elements in alsoinliers is > d
% this implies that we may have found a good model now test how good it is
bettermodel = model parameters fitted to all points in maybeinliers and alsoinliers
thiserr = a measure of how well model fits these points
if thiserr < besterr
bestfit = bettermodel
besterr = thiserr
increment iterations
return bestfit
출처 : 인터넷 어딘가... 기억이 잘.. ㅡㅡ;