Clustering based on gaussian processes.
In this letter, we develop a gaussian process model for clustering. The variances of predictive values in gaussian processes learned from a training data are shown to comprise an estimate of the support of a probability density function. The constructed variance function is then applied to construct a set of contours that enclose the data points, which correspond to cluster boundaries. To perform clustering tasks of the data points, an associated dynamical system is built, and its topological invariant property is investigated. The experimental results show that the proposed method works successfully for clustering problems with arbitrary shapes.
Kim
Hyun-Chul
HC
Department of Computer Science, Yonsei University, 134 Shinchondong, Sudaimunku Seoul, 120-749, Korea. grass@postech.ac.kr
Lee
Jaewook
J
D016000:Cluster Analysis; D003205:Computing Methodologies; D015233:Models, Statistical; D016011:Normal Distribution