| 大会名称 |
|---|
| 2016年 ソサイエティ大会 |
| 大会コ－ド |
| 2016S |
| 開催年 |
| 2016 |
| 発行日 |
| 2016-09-06 |
| セッション番号 |
| BS-5 |
| セッション名 |
| Network and Service Design, Control and Management |
| 講演日 |
| 2016/9/22 |
| 講演場所(会議室等) |
| 工学部 M棟 M151 |
| 講演番号 |
| BS-5-26 |
| タイトル |
| IMAGE CLASSIFIER LEARNING FROM NOISY LABELS VIA GENERALIZED GRAPH SMOOTHNESS PRIORS |
| 著者名 |
| ○Yu Mao, Gene Cheung, Chia-wen Lin, Yusheng Ji, |
| キーワード |
| classification |
| 抄録 |
|
When collecting samples via crowd-sourcing for semi-supervised learning, often labels that designate events of interest are assigned unreliably, resulting in label noise. In this paper, we propose a robust method for graph-based image classifier learning given noisy labels, leveraging on recent advances in graph signal processing. In particular, we formulate a graph-signal restoration problem, where the objective includes a fidelity term to minimize the l0-norm between the observed labels and a reconstructed graph-signal, and generalized graph smoothness priors, where we assume that the reconstructed signal and its gradient are both smooth with respect to a graph. Simulation results show that our proposed algorithm can outperform both regular SVM and robust label noise learning approaches in the literature noticeably. |
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