Extended Semi-supervised Matrix Factorization for Clustering

Author

Xiaobing, Pei ; Shaohong, Fang ; Chuanbo, Chen

Author_Institution

Sch. of Software, HuaZhong Univ. of Sci. & Technol., Wuhan, China

Volume

2

fYear

2010

fDate

11-12 May 2010

Firstpage

281

Lastpage

284

Abstract

In this paper, we extend the Penalized Matrix Factorization (PMF) algorithm for semi-supervised clustering. The definition of may-link constraints are introduced and obtained based on must-link constraints and cluster structure. We derive the Extended PMF (EPMF) model by incorporating the may-link constraints inside the original PMF decomposition. Extensive experimental evaluations are performed on the SECTOR data set. The experimental results show the effectiveness of the extended PMF.

Keywords

learning (artificial intelligence); matrix decomposition; pattern clustering; extended semisupervised matrix factorization; matrix decomposition; penalized matrix factorization; semisupervised clustering; Automation; Clustering algorithms; Data mining; Digital images; Machine learning; Matrix decomposition; Performance evaluation; Software algorithms; Nonnegative matrix factorization; Penalized matrix factorization; Semi-supervised clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-7279-6

Electronic_ISBN

978-1-4244-7280-2

Type

conf

DOI

10.1109/ICICTA.2010.544

Filename

5523600