.png){kind=icon}
返回
Orthogonal optimal reverse prediction for semi-supervised learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2016.07.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Optimal reverse prediction (ORP) has recently been proposed as a semi-supervised framework to unify supervised and unsupervised training methods such as supervised least square, principal component analysis (PCA), k-means clustering and normalized graph-cut. ORP has an ability to deal with classification tasks in which the labeled data are insufficient. But, the performance of ORP and its kernelized version is still not satisfactory for classification applications. To further improve performance of ORP, motivated by recently proposed orthogonal k-means clustering, in this paper we propose an orthogonal optimal reverse prediction (OORP), together with its kernelized and Laplacian regularized extensions. With only limited additional computations, our algorithms can greatly enhance the classification performance, compared to the original ORP. Extensive experiments on synthetic and benchmark data collections consistently prove the effectiveness and efficiency of our OORP in comparison with several competing approaches. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Semi-supervised learning
Optimal reverse prediction
Orthogonal constraint
Orthogonal optimal reverse prediction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容