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- W2293583929 abstract "Multi-modal features are widely used to represent objects or events in pattern recognition and vision understanding. How to effectively integrate these heterogeneous features into a unified low-dimensional feature space has become a crucial issue in machine learning. In this work, we propose a novel approach which integrates heterogeneous features via an elaborate Semi-supervised Multi-Modal Deep Network (SMMDN). The proposed model first transforms the original data to high-level abstract homogeneous features. Then these homogeneous features are integrated into a new feature vector. By this means, our model can obtain abstract fused representations with lower-dimensionality and stronger discriminative ability. A Series of experiments are conducted on two object recognition datasets. Results show that our approach can integrate heterogeneous features effectively and achieve better performance compared to other methods." @default.
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- W2293583929 date "2015-01-01" @default.
- W2293583929 modified "2023-09-24" @default.
- W2293583929 title "Heterogeneous Features Integration via Semi-supervised Multi-modal Deep Networks" @default.
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- W2293583929 doi "https://doi.org/10.1007/978-3-319-26561-2_2" @default.
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