  This paper deals with the estimation of the modes of an univariate mixture
when the number of components is known and when the component density are well
separated. We propose an algorithm based on the minimization of the "kp"
criterion we introduced in a previous work. In this paper we show that the
global minimum of this criterion can be reached with a linear least square
minimization followed by a roots finding algorithm. This is a major advantage
compared to classical iterative algorithms such as K-means or EM which suffer
from the potential convergence to some local extrema of the cost function they
use. Our algorithm performances are finally illustrated through simulations of
a five components mixture.
