Abstract
Subspace segmentation has been a hot topic in the past decades. Recently, spectral-clustering based methods arouse broad interests, however, they usually consider the similarity extraction in the original space. In this paper, we propose subspace learning based low-rank representation to learn a subspace favoring the similarity extraction for the low-rank representation. The process of learning the subspace and achieving the representation is conducted simultaneously and thus they can benefit from each other. After extending the linear projection to nonlinear mapping, our method can handle manifold clustering problem which is a general case of subspace segmentation. Moreover, our method can also be applied in the problem of recognition by adding suitable penalty on the learned subspace. Extensive experimental results confirm the effectiveness of our method.
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Acknowledgement
The work of K. Tang was supported by the Educational Commission of Liaoning Province, China (No. L201683662). The work of Z. Su was supported by the National Natural Science Foundation of China (No. 61572099), National Science and Technology Major Project (No. 2014ZX04001011, ZX20140419). The work of W. Jiang was supported by the Natural Science Foundation of Liaoning Province, China (No. 60875029).
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Tang, K., Liu, X., Su, Z., Jiang, W., Dong, J. (2017). Subspace Learning Based Low-Rank Representation. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_27
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