Abstract
GoDec+ shows its robustness in low-rank matrix decomposition but only deals with single-view data. This paper extends GoDec+ to multi-view data by jointly learning latent space and multi-view fusion feature. The proposed method factorizes the low-rank matrix in GoDec+ into the product of a basis matrix of the latent space and a shared representation given by a transformation matrix. By constraining the basis matrix to be group sparse, the proposed method treats the effects of different views differently. Extensive experiments show that the proposed method learns a good fusion feature and outperforms the compared methods in image classification and annotation.
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Acknowledgments
This work is supported in part by the National Natural Science Founding of China (61170192, U1636218), Science and Technology Planning Project of Guangzhou (201704020043), the Fundamental Research Funds for the Central Universities (2017MS045), and Guangzhou Key Lab of Body Data Science (201605030011).
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Guo, K., Xu, X., Cai, B., Zhang, T. (2018). Joint Latent Space and Multi-view Feature Learning. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_3
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DOI: https://doi.org/10.1007/978-981-10-8530-7_3
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