Sparsity inducing regularization is an important part for learning over-complete visual representations. Despite the popularity of $\ell_1$ regularization, in this paper, we investigate the usage of non-convex regularizations in this problem. Our contribution consists o... View more
[Boyd et al. 2011] Boyd, S.; Parikh, N.; Chu, E.; Peleato, B.; and Eckstein, J. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends R in Machine Learning 3(1):1- 122.
[Chen, Zhou, and Ye 2011] Chen, J.; Zhou, J.; and Ye, J.
2011. Integrating low-rank and group-sparse structures for robust multi-task learning. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 42-50. ACM.
[Cootes et al. 1995] Cootes, T. F.; Taylor, C. J.; Cooper, D. H.; and Graham, J. 1995. Active shape models-their training and application. Computer vision and image understanding 61(1):38-59.
[Fan and Li 2011] Fan, J., and Li, R. 2011. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96(456):1348-1360.
[Frank et al. 1993] Frank, I. E.; Friedman, J. H.; Wold, S.; Hastie, T.; and Mallows, C. 1993. A statistical view of some chemometrics regression tools. discussion. author's reply. Technometrics 35(2):109-148.
[Friedman 2012] Friedman, J. H. 2012. Fast sparse regression and classification. International Journal of Forecasting 28(3):722-738.
[Gong, Ye, and Zhang 2012] Gong, P.; Ye, J.; and Zhang, C.- s. 2012. Multi-stage multi-task feature learning. In Advances in Neural Information Processing Systems, 1988- 1996.
[Han and Zhang 2016] Han, L., and Zhang, Y. 2016. Multistage multi-task learning with reduced rank. In AAAI, 1638- 1644.
[Hassibi, Stork, and others 1993] Hassibi, B.; Stork, D. G.; et al. 1993. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems 164-164.