.png){kind=icon}
返回
Enhancing semi-supervised learning through label-aware base kernels
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.07.072.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Currently, a large family of kernel design methods for semi-supervised learning (SSL) problems builds the kernel by weighted averaging of predefined base kernels (i.e., those spanned by kernel eigenvectors). Optimization of the base kernel weights has been studied extensively in the literature. However, little attention was devoted to designing high-quality base kernels. The eigenvectors of the kernel matrix, which are computed irrespective of class labels, may not always reveal useful structures of the target. As a result, the generalization performance can be poor however hard the base kernel weighting is tuned. On the other hand, there are many SSL algorithms whose focus are not on kernel design but on the estimation of the class labels directly. Motivated by the label propagation approach, in this paper we propose to construct novel kernel eigenvectors by injecting the class label information under the framework of eigenfunction extrapolation. A set of label-aware base kernels can be obtained with greatly improved quality, which leads to higher target alignment and henceforth better performance. Our approach is computationally efficient, and demonstrates encouraging performance in semi-supervised classification and regression tasks. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Semi-supervised learning
Multiple-kernel learning
Kernel target alignment
Label-aware base kernels
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
查看更多机构
引用论文
On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
PREAI
分享收藏Characterization of 8p21.3 chromosomal deletions in B-cell lymphoma: TRAIL-R1 and TRAIL-R2 as candidate dosage-dependent tumor suppressor genes.png)
Blood
OAAI
BloodIF0
OAAI分享收藏分享收藏分享收藏
分享收藏分享收藏 没有更多内容