An improved learning with local and global consistency

Author

Li, Ming ; Zhang, Xiaoli ; Wang, Xuesong

Author_Institution

Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China

fYear

2010

fDate

26-28 May 2010

Firstpage

1152

Lastpage

1156

Abstract

Learning with local and global consistency (LLGC) algorithm can effectively label a data, but it is helpless for noise data. The reason is that the LLGC algorithm will predict a label for each unlabelled data without taking into account whether a data has noise or not. Aiming at the deficiency of the LLGC algorithm, an improved version for semi-supervised learning algorithm with local and global consistency is proposed in this paper. At first, we compute the similarity of each data to all classes. And then the data can be ascribed to one class according to its similarities. The improved LLGC algorithm not only can label data as the conventional LLGC, but also can identify noise existed in data set effectively. Simulation results show that the improved LLGC algorithm can effectively avoid noise data being viewed as normal data.

Keywords

learning (artificial intelligence); LLGC algorithm; global consistency; improved learning; local consistency; noise data; semisupervised learning algorithm; unlabelled data; Clustering algorithms; Computational modeling; Electronic mail; Learning systems; Machine learning; Machine learning algorithms; Prediction algorithms; Semisupervised learning; Supervised learning; Unsupervised learning; Graph; Learning with local and global consistency; Noise data; Semi-supervised learning; Similarity;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498148

Filename

5498148