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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Unified subspace learning for incomplete and unlabeled multi-view data
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| 英文论文题目: |
Unified subspace learning for incomplete and unlabeled multi-view data
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| 论文题目英文: |
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| 作者: |
Yin, Qiyue
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| 论文出处: |
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| 刊物名称: |
PATTERN RECOGNITION
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| 年: |
2017
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| 卷: |
67 |
| 期: |
67 |
| 页: |
313-327 |
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| 影响因子: |
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| 摘要: |
Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In this paper, we propose a novel subspace learning framework for incomplete and unlabeled multi-view data. The model directly optimizes the class indicator matrix, which establishes a bridge for incomplete feature sets. Besides, feature selection is considered to deal with high dimensional and noisy features. Furthermore, the inter-view and intra-view data similarities are preserved to enhance the model. To these ends, an objective is developed along with an efficient optimization strategy. Finally, extensive experiments are conducted for multi-view clustering and cross-modal retrieval, achieving the state-of-the-art performance under various settings. (C) 2017 Elsevier Ltd. All rights reserved. |
| 英文摘要: |
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