@inproceedings{29841c14e3df4c37a6f0cab10839d40e,
title = "Nonnegative Lagrangian relaxation of K-means and spectral clustering",
abstract = "We show that K-means and spectral clustering objective functions can be written as a trace of quadratic forms. Instead of relaxation by eigenvectors, we propose a novel relaxation maintaining the nonnegativity of the cluster indicators and thus give the cluster posterior probabilities, therefore resolving cluster assignment difficulty in spectral relaxation. We derive a multiplicative updating algorithm to solve the nonnegative relaxation problem. The method is briefly extended to semi-supervised classification and semi-supervised clustering.",
author = "Chris Ding and Xiaofeng He and Simon, \{Horst D.\}",
year = "2005",
doi = "10.1007/11564096\_51",
language = "英语",
isbn = "3540292438",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "530--538",
booktitle = "Machine Learning - ECML 2005",
address = "德国",
note = "16th European Conference on Machine Learning, ECML 2005 ; Conference date: 03-10-2005 Through 07-10-2005",
}