Algebraic Variety Models for High-Rank Matrix Completion

MATLAB code to implement the variety-based matrix completion (VMC) algorithm described in the paper:

G. Ongie, R. Willett, R. Nowak, L. Balzano. "Algebraic Variety Models for High-Rank Matrix Completion", in ICML 2017. Available online: https://arxiv.org/abs/1703.09631

The main file is vmc.m. To get started, see the example scripts:

• example_uos_sm.m - Small-scale union-of-subspaces data
• example_uos_lrg.m - Large-scale union-of-subspaces data
• example_hopkins.m - Small-scale example using Hopkins 155 dataset
• example_mocap.m - Large-scale example using CMU Mocap dataset

Version history

• Version 0.1, Updated 7/22/2017

Author

Greg Ongie (website)

Acknowledgments

Datasets used in the examples are borrowed from:

• Hopkins 155: http://www.vision.jhu.edu/data/hopkins155/
• CMU Mocap: http://mocap.cs.cmu.edu/

Our implementation of randomized svd uses code from:

• Mu Li: Making Large-Scale Nyström Approximation Possible
• Antoine Liutkus: Matlab Central