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- W2588822028 abstract "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." @default.
- W2588822028 created "2017-02-24" @default.
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- W2588822028 date "2017-07-01" @default.
- W2588822028 modified "2023-10-09" @default.
- W2588822028 title "Unified subspace learning for incomplete and unlabeled multi-view data" @default.
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- W2588822028 doi "https://doi.org/10.1016/j.patcog.2017.01.035" @default.
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