.png){kind=icon}
返回
Semi-supervised classification with pairwise constraints
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2014.02.053.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph-based semi-supervised learning has been intensively investigated for a long history. However, existing algorithms only utilize the similarity information between examples for graph construction, so their discriminative ability is rather limited. In order to overcome this limitation, this paper considers both similarity and dissimilarity constraints, and constructs a signed graph with positive and negative edge weights to improve the classification performance. Therefore, the proposed algorithm is termed as Constrained Semi-supervised Classifier (CSSC). A novel smoothness regularizer is proposed to make the must-linked examples obtain similar labels, and cannot-linked examples get totally different labels. Experiments on a variety of synthetic and real-world datasets demonstrate that CSSC achieves better performances than some state-of-the-art semi-supervised learning algorithms, such as Harmonic Functions, Linear Neighborhood Propagation, LapRLS, LapSVM, and Safe Semi-supervised Support Vector Machines. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Semi-supervised learning
Pairwise constraints
Smoothness regularizer
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
A review of the Irish crustal structure and signatures from the Caledonian and Variscan Orogenies.png)
Terra Nova
Terra NovaIF0
PREAI
分享收藏Pairwise constraints based multiview features fusion for scene classificationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏分享收藏
查看更多内容