Projected clustering via robust orthogonal least square regression with optimal scaling | |
Zhang, Rui1; Nie, Feiping1; Li, Xuelong2 | |
2017 | |
会议日期 | 2017-05-14 |
会议地点 | Anchorage, AK, United states |
卷号 | 2017-May |
DOI | 10.1109/IJCNN.2017.7966199 |
页码 | 2784-2791 |
英文摘要 | The orthogonal least square regression (OLSR) serves as a pretty significant problem for the dimensionality reduction. Due to lack of the scale change in OLSR, the scaling term is at first introduced to OLSR to build up a novel orthogonal least square regression with optimal scaling (OLSR-OS) problem. However, OLSR-OS is still sensitive to the outliers, such that associated results could be fallacious. To strengthen the robustness of OLSR-OS, we propose an original robust OLSR-OS (ROLSR-OS) problem in 2,1-norm. To tackle a more ill-defined situation, ROLSR-OS in 2,1-norm can be further extended to ROLSR-OS in capped 2-norm. Besides, the associated ROLSR-OS methods could be derived by solving the re-weighted counterparts of ROLSR-OS problems in both norms. Moreover, the equivalence between the re-weighted counterparts and the original ROLSR-OS problems is also provided along with the convergence analysis of the proposed ROLSR-OS methods. Accordingly, both the optimal scaling and weight can be achieved automatically via the proposed ROLSR-OS approaches. Specifically, the proposed ROLSR-OS methods are self-adaptive, such that the smaller weight would be automatically assigned to the term with larger outliers to enhance the robustness. Consequently, projected clustering and modified projected clustering under the proposed ROLSR-OS problems are further investigated both theoretically and experimentally. © 2017 IEEE. |
产权排序 | 2 |
会议录 | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings |
会议录出版者 | Institute of Electrical and Electronics Engineers Inc. |
语种 | 英语 |
ISBN号 | 9781509061815 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/29408] |
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.School of Computer Science, Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China 2.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China |
推荐引用方式 GB/T 7714 | Zhang, Rui,Nie, Feiping,Li, Xuelong. Projected clustering via robust orthogonal least square regression with optimal scaling[C]. 见:. Anchorage, AK, United states. 2017-05-14. |
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