Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds

Yuanyuan Liu CUHK, Fanhua Shang, Hong Cheng, James Cheng
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:715-724, 2014.

Abstract

This paper aims to address a class of nuclear norm regularized least square (NNLS) problems. By exploiting the underlying low-rank matrix manifold structure, the problem with nuclear norm regularization is cast to a Riemannian opti- mization problem over matrix manifolds. Com- pared with existing NNLS algorithms involving singular value decomposition (SVD) of large- scale matrices, our method achieves significant reduction in computational complexity. More- over, the uniqueness of matrix factorization can be guaranteed by our Grassmannian manifold method. In our solution, we first introduce the bilateral factorization into the original NNLS problem and convert it into a Grassmannian op- timization problem by using a linearized tech- nique. Then the conjugate gradient procedure on the Grassmannian manifold is developed for our method with a guarantee of local convergence. Finally, our method can be extended to address the graph regularized problem. Experimental re- sults verified both the efficiency and effective- ness of our method.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-cuhk14a, title = {Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds}, author = {CUHK, Yuanyuan Liu and Shang, Fanhua and Cheng, Hong and Cheng, James}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {715--724}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/cuhk14a/cuhk14a.pdf}, url = {https://proceedings.mlr.press/r12/cuhk14a.html}, abstract = {This paper aims to address a class of nuclear norm regularized least square (NNLS) problems. By exploiting the underlying low-rank matrix manifold structure, the problem with nuclear norm regularization is cast to a Riemannian opti- mization problem over matrix manifolds. Com- pared with existing NNLS algorithms involving singular value decomposition (SVD) of large- scale matrices, our method achieves significant reduction in computational complexity. More- over, the uniqueness of matrix factorization can be guaranteed by our Grassmannian manifold method. In our solution, we first introduce the bilateral factorization into the original NNLS problem and convert it into a Grassmannian op- timization problem by using a linearized tech- nique. Then the conjugate gradient procedure on the Grassmannian manifold is developed for our method with a guarantee of local convergence. Finally, our method can be extended to address the graph regularized problem. Experimental re- sults verified both the efficiency and effective- ness of our method.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds %A Yuanyuan Liu CUHK %A Fanhua Shang %A Hong Cheng %A James Cheng %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-cuhk14a %I PMLR %P 715--724 %U https://proceedings.mlr.press/r12/cuhk14a.html %V R12 %X This paper aims to address a class of nuclear norm regularized least square (NNLS) problems. By exploiting the underlying low-rank matrix manifold structure, the problem with nuclear norm regularization is cast to a Riemannian opti- mization problem over matrix manifolds. Com- pared with existing NNLS algorithms involving singular value decomposition (SVD) of large- scale matrices, our method achieves significant reduction in computational complexity. More- over, the uniqueness of matrix factorization can be guaranteed by our Grassmannian manifold method. In our solution, we first introduce the bilateral factorization into the original NNLS problem and convert it into a Grassmannian op- timization problem by using a linearized tech- nique. Then the conjugate gradient procedure on the Grassmannian manifold is developed for our method with a guarantee of local convergence. Finally, our method can be extended to address the graph regularized problem. Experimental re- sults verified both the efficiency and effective- ness of our method. %Z Reissued by PMLR on 04 October 2026.
APA
CUHK, Y.L., Shang, F., Cheng, H. & Cheng, J.. (2014). Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:715-724 Available from https://proceedings.mlr.press/r12/cuhk14a.html. Reissued by PMLR on 04 October 2026.

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