This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Pearson K. On lines and planes of closest fit to systems of points in space. Philos Mag, 1901, 2: 559–572
Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process, 2006, 15: 3736–3745
Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process, 2006, 54: 4311–4322
Zhao Q, Meng D Y, Xu Z B. Robust sparse principal component analysis. Sci China Inf Sci, 2014, 57: 092115
Deledalle C A, Denis L, Tupin F. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans Image Process, 2009, 18: 2661–2672
Zhang Z, Li F, Zhao M, et al. Joint low-rank and sparse principal feature coding for enhanced robust representation and visual classification. IEEE Trans Image Process, 2016, 25: 2429–2443
This work was supported by National Natural Science Foundation of China (Grant No. 60872131). The idea of the principal basis analysis presented here arises through a lot of deep discussions with Professor Henri Maître at Telecom-ParisTech in France. We are also grateful to Prof. Didier Le Ruyet at CNAM in France for many fruitful discussions.
The authors declare that they have no conflict of interest.
About this article
Cite this article
Sun, H., Sang, C. & Liu, C. Principal basis analysis in sparse representation. Sci. China Inf. Sci. 60, 028102 (2017). https://doi.org/10.1007/s11432-015-0960-8