Clustering based on Gaussian processes
SCIE
SCOPUS
- Title
- Clustering based on Gaussian processes
- Authors
- Kim, HC; Lee, J
- Date Issued
- 2007-11
- Publisher
- M I T PRESS
- Abstract
- 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.
- Keywords
- CLASSIFICATION
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/23155
- DOI
- 10.1162/neco.2007.19.11.3088
- ISSN
- 0899-7667
- Article Type
- Article
- Citation
- NEURAL COMPUTATION, vol. 19, no. 11, page. 3088 - 3107, 2007-11
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.