Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization
文献类型：期刊论文
| 作者 | Yiu-ming Cheung; Chuang Lin; Jian Lou; Meng Pang; Risheng Liu |
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | The key ingredients of matrix factorization lie in the basis learning and coefficient representation. To enhance the discriminant ability of the learnt basis, discriminant graph embedding is usually introduced in matrix factorization model. However, existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graphs, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., L 1 -graph), while ignoring the cooperation of different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs—One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discrimi- nant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the pro |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14189]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Yiu-ming Cheung,Chuang Lin,Jian Lou,et al. Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2018. |
| APA | Yiu-ming Cheung,Chuang Lin,Jian Lou,Meng Pang,&Risheng Liu.(2018).Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. |
| MLA | Yiu-ming Cheung,et al."Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
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