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Algorithms 2015, 8(4), 1021-1034; doi:10.3390/a8041021

Semi-Supervised Classification Based on Mixture Graph

1
and
1,2,*
1
College of Computer and Information Science, Southwest University, Chongqing 400715, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Received: 2 August 2015 / Revised: 3 November 2015 / Accepted: 5 November 2015 / Published: 16 November 2015
View Full-Text   |   Download PDF [329 KB, uploaded 16 November 2015]   |  

Abstract

Graph-based semi-supervised classification heavily depends on a well-structured graph. In this paper, we investigate a mixture graph and propose a method called semi-supervised classification based on mixture graph (SSCMG). SSCMG first constructs multiple k nearest neighborhood (kNN) graphs in different random subspaces of the samples. Then, it combines these graphs into a mixture graph and incorporates this graph into a graph-based semi-supervised classifier. SSCMG can preserve the local structure of samples in subspaces and is less affected by noisy and redundant features. Empirical study on facial images classification shows that SSCMG not only has better recognition performance, but also is more robust to input parameters than other related methods. View Full-Text
Keywords: semi-supervised classification; graph construction; subspaces; mixture graph semi-supervised classification; graph construction; subspaces; mixture graph
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Feng, L.; Yu, G. Semi-Supervised Classification Based on Mixture Graph. Algorithms 2015, 8, 1021-1034.

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