Subspace clustering via adaptive-loss regularized representation learning with latent affinities
Author:
Funder
Natural Science Basic Research Program of Shaanxi Province
Shaanxi Province Key Research and Development Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10044-024-01226-7.pdf
Reference47 articles.
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3. Elhamifar E, Vidal R (2009) Sparse subspace clustering. In: 2009 IEEE conference on computer vision and pattern recognition, pp 2790–2797. https://doi.org/10.1109/CVPR.2009.5206547
4. Fang X, Xu Y, Li X, Fan Z, Liu H, Chen Y (2014) Locality and similarity preserving embedding for feature selection. Neurocomputing 128:304–315. https://doi.org/10.1016/j.neucom.2013.08.040
5. Fang X, Xu Y, Li X, Lai Z, Wong WK (2016) Robust semi-supervised subspace clustering via non-negative low-rank representation. IEEE Trans Cybern 46(8):1828–1838. https://doi.org/10.1109/TCYB.2015.2454521
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