Name
Title | ||
|---|---|---|
Joint feature representation and classification via adaptive graph semi-supervised nonnegative matrix factorization. |
Abstract | ||
|---|---|---|
As an effective feature representation method, non-negative matrix factorization (NMF) cannot utilize the label information sufficiently, which makes it not be suitable for the classification task. In this paper, we propose a joint feature representation and classification framework named adaptive graph semi-supervised nonnegative matrix factorization (AGSSNMF). Firstly, to enhance the discriminative ability of feature representation and accomplish the classification task, a regression model with nonnegative matrix factorization (called as RNMF) is proposed, which exploits the relation between the label information and feature representation. Secondly, to overcome the drawback of insufficient labels, an adaptive graph-based label propagation (refereed as AGLP) model is established, which adopts a local constraint to reflect the local structure of data. Then, we integrate RNMF and AGLP into a unified framework for feature representation and classification. Finally, an iterative optimization algorithm is used to solve the objective function. Extensive experiments show that the proposed framework has excellent performance compared with some well-known methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.image.2020.115984 | SIGNAL PROCESSING-IMAGE COMMUNICATION |
Keywords | DocType | Volume |
Feature representation,Nonnegative matrix factorization,Adaptive graph,Label propagation,Classification | Journal | 89 |
ISSN | Citations | PageRank |
0923-5965 | 1 | 0.35 |
References | Authors | |
34 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yugen Yi | 1 | 92 | 15.25 |
| Yuqi Chen | 2 | 2 | 1.37 |
| Jianzhong Wang | 3 | 16 | 1.86 |
| Gang Lei | 4 | 1 | 0.68 |
| Jiangyan Dai | 5 | 14 | 4.19 |
| Huihui Zhang | 6 | 1 | 0.35 |