The search functionality is under construction.
The search functionality is under construction.

Direct Log-Density Gradient Estimation with Gaussian Mixture Models and Its Application to Clustering

Qi ZHANG, Hiroaki SASAKI, Kazushi IKEDA

  • Full Text Views

    0

  • Cite this

Summary :

Estimation of the gradient of the logarithm of a probability density function is a versatile tool in statistical data analysis. A recent method for model-seeking clustering called the least-squares log-density gradient clustering (LSLDGC) [Sasaki et al., 2014] employs a sophisticated gradient estimator, which directly estimates the log-density gradients without going through density estimation. However, the typical implementation of LSLDGC is based on a spherical Gaussian function, which may not work well when the probability density function for data has highly correlated local structures. To cope with this problem, we propose a new gradient estimator for log-density gradients with Gaussian mixture models (GMMs). Covariance matrices in GMMs enable the new estimator to capture the highly correlated structures. Through the application of the new gradient estimator to mode-seeking clustering and hierarchical clustering, we experimentally demonstrate the usefulness of our clustering methods over existing methods.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.6 pp.1154-1162
Publication Date
2019/06/01
Publicized
2019/03/22
Online ISSN
1745-1361
DOI
10.1587/transinf.2018EDP7354
Type of Manuscript
PAPER
Category
Artificial Intelligence, Data Mining

Authors

Qi ZHANG
  Graduate University for Advanced Studies
Hiroaki SASAKI
  Nara Institute of Science and Technology
Kazushi IKEDA
  Nara Institute of Science and Technology

Keyword