[1]徐光生,王士同.基于双重低秩分解的不完整多视图子空间学习[J].智能系统学报,2022,17(6):1084-1092.[doi:10.11992/tis.202107002]
XU Guangsheng,WANG Shitong.Incomplete multi-view subspace learning through dual low-rank decompositions[J].CAAI Transactions on Intelligent Systems,2022,17(6):1084-1092.[doi:10.11992/tis.202107002]
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XU Guangsheng,WANG Shitong.Incomplete multi-view subspace learning through dual low-rank decompositions[J].CAAI Transactions on Intelligent Systems,2022,17(6):1084-1092.[doi:10.11992/tis.202107002]
基于双重低秩分解的不完整多视图子空间学习
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第6期
页码:
1084-1092
栏目:
学术论文—机器学习
出版日期:
2022-11-05
- Title:
- Incomplete multi-view subspace learning through dual low-rank decompositions
- Keywords:
- subspace learning; supervised learning; incomplete multi-view; latent factors; low-rank constraint; dual low-rank decompositions; feature alignment; low-dimensional feature
- 分类号:
- TP181
- 文献标志码:
- 2022-10-10
- 摘要:
- 多视图数据在现实世界中应用广泛，各种视角和不同的传感器有助于更好的数据表示，然而，来自不同视图的数据具有较大的差异，尤其当多视图数据不完整时，可能导致训练效果较差甚至失败。为了解决该问题，本文提出了一个基于双重低秩分解的不完整多视图子空间学习算法。所提算法通过两方面来解决不完整多视图问题：一方面，基于双重低秩分解子空间框架，引入潜在因子来挖掘多视图数据中缺失的信息；另一方面，通过预先学习的多视图数据低维特征获得更好的鲁棒性，并以有监督的方式来指导双重低秩分解。实验结果证明，所提算法较之前的多视图子空间学习算法有明显优势；即使对于不完整的多视图数据，该算法也具有良好的分类性能。
- Abstract:
- Multi-view data are very common in real-world applications. Different viewpoints and sensors tend to facilitate better data representation. However, data from various perspectives show a significant variation. Especially when only incomplete multi-view data are available, the corresponding multi-view learning may result in poor performance or even training failure. This study proposes a multi-view learning algorithm called IMSL (Incomplete Multi-View Subspace Learning through Dual Low-Rank Decompositions) to tackle this issue. The proposed algorithm addresses the incomplete multi-view problem in two ways: (1) Latent factors are introduced into a dual low-rank decomposition subspace framework to mine missing information in the multi-view data. (2) IMSL seeks a more robust subspace through pre-learned low-dimensional features of multi-view data. Furthermore, the supervised data are used to guide dual low-rank decompositions. Experimental results show that the proposed algorithm outperforms the previous multi-view subspace learning algorithms on the adopted incomplete multi-view datasets. Pre-learning low-dimensional features of multi-view data, on the other hand, can improve robustness, and dual low-rank decomposition can be guided in a supervised manner.
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备注/Memo
收稿日期:2021-07-01。
基金项目:江苏省自然科学基金项目(BK20191331).
作者简介:徐光生,硕士研究生,主要研究方向为人工智能、机器学习;王士同，教授，博士生导师，主要研究方向为人工智能、模式识别。发表学术论文近百篇
通讯作者:王士同.E-mail:wxwangst@aliyun.com
更新日期/Last Update:
1900-01-01