![]() In Proceedings of the IEEE international conference on computer vision, pp. Li P, Wang Q, Zuo W, Zhang L (2013) Log-Euclidean kernels for sparse representation and dictionary learning. IEEE Trans Pattern Anal Mach Intell 27(5):684–98 ![]() Lee KC, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. Kang Z, Pan H, Hoi SC, Xu Z (2019) Robust graph learning from noisy data. Kang Z, Lin Z, Zhu X, Xu W (2021) Structured graph learning for scalable subspace clustering: from single view to multiview. In Proceedings Eighth IEEE International Conference on computer Vision, pp. Kanatani KI (2001) Motion segmentation by subspace separation and model selection. Pacific-Asia conference on knowledge discovery and data mining. Jing L, Ng MK, Xu J, Huang JZ (2005) Subspace clustering of text documents with feature weighting k-means algorithm. Jia H, Wang L, Song H, Mao Q, Ding S (2021) An efficient Nyström spectral clustering algorithm using incomplete Cholesky decomposition. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp 1–7 Goh A, Vidal R (2008) Clustering and dimensionality reduction on Riemannian manifolds. IEEE Trans Pattern Anal Mach Intell 35(11):2765–81įavaro P, Vidal R, Ravichandran A (2011) A closed form solution to robust subspace estimation and clustering. J Mach Learn Res 14(1):2487–517Įlhamifar E, Vidal R (2013) Sparse subspace clustering: Algorithm, theory, and applications. Proc Natl Acad Sci 100:5591–5596ĭyer EL, Sankaranarayanan AC, Baraniuk RG (2013) Greedy feature selection for subspace clustering. SIAM J Imag Sci 9(4):1582–618ĭonoho D (2003) Hessian eigenmaps: new tools for nonlinear dimensionality reduction. IEEE Signal Process Lett 25(2):164–8Ĭhoi GP, Ho KT, Lui LM (2016) Spherical conformal parameterization of genus-0 point clouds for meshing. BirkhäuserĬasselman B (2014) Stereographic projection, Feature columnĬhen Y, Li G, Gu Y (2017) Active orthogonal matching pursuit for sparse subspace clustering. Adv Neural Inform Process Syst 14:585–591Ĭarmo MP (1992) Riemannian Geom. PMLR, pp 561–568īelkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: International Conference on Machine Learning. Pattern Recogn 118:108041īai L, Liang J (2020) Sparse subspace clustering with entropy-norm. Chapman
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |