Semi-Supervised Classification Based on Mixture Graph
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 diffe...
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Online Access: | http://www.mdpi.com/1999-4893/8/4/1021 |
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doaj-3d4d8fd640c045b58a26fc1affdc5a912020-11-25T00:50:09ZengMDPI AGAlgorithms1999-48932015-11-01841021103410.3390/a8041021a8041021Semi-Supervised Classification Based on Mixture GraphLei Feng0Guoxian Yu1College of Computer and Information Science, Southwest University, Chongqing 400715, ChinaCollege of Computer and Information Science, Southwest University, Chongqing 400715, ChinaGraph-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.http://www.mdpi.com/1999-4893/8/4/1021semi-supervised classificationgraph constructionsubspacesmixture graph |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Feng Guoxian Yu |
spellingShingle |
Lei Feng Guoxian Yu Semi-Supervised Classification Based on Mixture Graph Algorithms semi-supervised classification graph construction subspaces mixture graph |
author_facet |
Lei Feng Guoxian Yu |
author_sort |
Lei Feng |
title |
Semi-Supervised Classification Based on Mixture Graph |
title_short |
Semi-Supervised Classification Based on Mixture Graph |
title_full |
Semi-Supervised Classification Based on Mixture Graph |
title_fullStr |
Semi-Supervised Classification Based on Mixture Graph |
title_full_unstemmed |
Semi-Supervised Classification Based on Mixture Graph |
title_sort |
semi-supervised classification based on mixture graph |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2015-11-01 |
description |
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. |
topic |
semi-supervised classification graph construction subspaces mixture graph |
url |
http://www.mdpi.com/1999-4893/8/4/1021 |
work_keys_str_mv |
AT leifeng semisupervisedclassificationbasedonmixturegraph AT guoxianyu semisupervisedclassificationbasedonmixturegraph |
_version_ |
1725249109989785600 |