中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 紫东太初大模型研究中心
  5. 图像与视频分析团队
Semi-supervised Unified Latent Factor learning with multi-view data

文献类型：期刊论文

作者Jiang, Yu1; Liu, Jing1; Li, Zechao2; Lu, Hanqing1
刊名MACHINE VISION AND APPLICATIONS
出版日期2014-10-01
卷号25期号:7页码:1635-1645
关键词Multi-view learning Semi-supervised learning Unified latent factor learning Nonnegative matrix factorization
英文摘要Explosive multimedia resources are generated on web, which can be typically considered as a kind of multi-view data in nature. In this paper, we present a Semi-supervised Unified Latent Factor learning approach (SULF) to learn a predictive unified latent representation by leveraging both complementary information among multiple views and the supervision from the partially label information. On one hand, SULF employs a collaborative Nonnegative Matrix Factorization formulation to discover a unified latent space shared across multiple views. On the other hand, SULF adopts a regularized regression model to minimize a prediction loss on partially labeled data with the latent representation. Consequently, the obtained parts-based representation can have more discriminating power. In addition, we also develop a mechanism to learn the weights of different views automatically. To solve the proposed optimization problem, we design an effective iterative algorithm. Extensive experiments are conducted for both classification and clustering tasks on three real-world datasets and the compared results demonstrate the superiority of our approach.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]NONNEGATIVE MATRIX FACTORIZATION ; DIMENSIONALITY REDUCTION
收录类别SCI
语种英语
WOS记录号WOS:000342435800002
源URL[http://ir.ia.ac.cn/handle/173211/3358]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Nanjing Univ Sci & Technol, Sch Comp Sci, Nanjing, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Yu,Liu, Jing,Li, Zechao,et al. Semi-supervised Unified Latent Factor learning with multi-view data[J]. MACHINE VISION AND APPLICATIONS,2014,25(7):1635-1645.
APA Jiang, Yu,Liu, Jing,Li, Zechao,&Lu, Hanqing.(2014).Semi-supervised Unified Latent Factor learning with multi-view data.MACHINE VISION AND APPLICATIONS,25(7),1635-1645.
MLA Jiang, Yu,et al."Semi-supervised Unified Latent Factor learning with multi-view data".MACHINE VISION AND APPLICATIONS 25.7(2014):1635-1645.

入库方式： OAI收割

来源：自动化研究所

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。