Estimation of high-density regions using One-Class Neighbor Machines

Alberto Muñoz; Javier M Moguerza

doi:10.1109/TPAMI.2006.52

Estimation of high-density regions using One-Class Neighbor Machines

IEEE Trans Pattern Anal Mach Intell. 2006 Mar;28(3):476-80. doi: 10.1109/TPAMI.2006.52.

Authors

Alberto Muñoz¹, Javier M Moguerza

Affiliation

¹ Department of Statistics, University Carlos III, Getafe, Madrid, Spain. alberto.munoz@uc3m.es

PMID: 16526433
DOI: 10.1109/TPAMI.2006.52

Abstract

In this paper, we investigate the problem of estimating high-density regions from univariate or multivariate data samples. We estimate minimum volume sets, whose probability is specified in advance, known in the literature as density contour clusters. This problem is strongly related to One-Class Support Vector Machines (OCSVM). We propose a new method to solve this problem, the One-Class Neighbor Machine (OCNM) and we show its properties. In particular, the OCNM solution asymptotically converges to the exact minimum volume set prespecified. Finally, numerical results illustrating the advantage of the new method are shown.

MeSH terms

Algorithms*
Artificial Intelligence*
Diagnosis, Computer-Assisted / methods*
Humans
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Information Storage and Retrieval / methods*
Neoplasms / diagnosis*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity