热门
Block-Row Sparse Multiview Multilabel Learning for Image Classification
文献类型：期刊论文
| 作者 | Zhu, Xiaofeng1,2; Li, Xuelong3 ; Zhang, Shichao4
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016-02-01 |
| 卷号 | 46期号:2页码:450-461 |
| ISSN号 | 2168-2267 |
| 关键词 | Feature selection image classification joint sparse learning machine learning multiview learning |
| 通讯作者 | zhang, sc |
| 产权排序 | 3 |
| 英文摘要 | in image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. thus, learning with all the derived views of the data could decrease the effectiveness. to address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (mvml) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the mvml framework. the block-row regularizer concatenating a frobenius norm (f-norm) regularizer and an l(2,1)-norm regularizer is designed to conduct a hierarchical feature selection, in which the f-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the l(2,1)-norm regularizer is then used to conduct a low-level feature selection on the informative views. the rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the f-norm regularizer), and to remove noisy features (the l(2,1)-norm regularizer), respectively. we further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance. |
| 学科主题 | computer science, artificial intelligence ; computer science, cybernetics |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | regression ; selection |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000370962900011 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27858]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China 2.Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China 4.Zhejiang Gongshang Univ, Sch Comp Sci & Informat Technol, Hangzhou 310018, Zhejiang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Xiaofeng,Li, Xuelong,Zhang, Shichao. Block-Row Sparse Multiview Multilabel Learning for Image Classification[J]. ieee transactions on cybernetics,2016,46(2):450-461. |
| APA | Zhu, Xiaofeng,Li, Xuelong,&Zhang, Shichao.(2016).Block-Row Sparse Multiview Multilabel Learning for Image Classification.ieee transactions on cybernetics,46(2),450-461. |
| MLA | Zhu, Xiaofeng,et al."Block-Row Sparse Multiview Multilabel Learning for Image Classification".ieee transactions on cybernetics 46.2(2016):450-461. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。