Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10361672 | Pattern Recognition Letters | 2005 | 11 Pages |
Abstract
This paper addresses a new method and aspect of information-theoretic clustering where we exploit the minimum entropy principle and the quadratic distance measure between probability densities. We present a new minimum entropy objective function which leads to the maximization of within-cluster association. A simple implementation using the gradient ascent method is given. In addition, we show that the minimum entropy principle leads to the objective function of the k-means clustering, and the maximum within-cluster association is closed related to the spectral clustering which is an eigen-decomposition-based method. This information-theoretic view of spectral clustering leads us to use the kernel density estimation method in constructing an affinity matrix.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Yongjin Lee, Seungjin Choi,