.png){kind=icon}
返回
Semi-supervised subclass support vector data description for image and video classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.08.028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, the Semi-Supervised Subclass Support Vector Data Description is presented, a method that operates in both the supervised and the semi-supervised One-class classification case. The proposed method consists a novel extension of the standard SVDD method, by introducing two additional terms its optimization problem. These two terms correspond to expressing global and local geometric data information respectively, during the classifier optimization process. Global geometric data information is employed by minimizing the global target class variance, assuming that subclasses may have been formed within as well. In addition, by exploiting the semi-supervised learning smoothness assumption, local neighborhood information between all available (labeled and unlabeled) data is preserved, even in the supervised learning case. We show that the adoption of both terms results in a regularized feature space, where low variance directions have been emphasized, while local geometric data information have been preserved. The proposed method has been evaluated in classification problems related to face recognition, human action recognition and generic One-class classification problems, comparing favorably against related One-class classification methods in both the semi-supervised and the supervised learning cases. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
One-class classification
Support vector data description
Semi-supervised SVDD
Subclass SVDD
(SSVDD)-S-3
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏
A K-Farthest-Neighbor-based approach for support vector data description.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏Multi-class Support Vector Machine classifiers using intrinsic and penalty graphsPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Regularized extreme learning machine for multi-view semi-supervised action recognitionNEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏
查看更多内容