Scaling up your high dimensional regression by reducing the dimensionality using (is this always true?) a random projection, which is not far from compressed sensing, except in how we handle noise. In this linear case, this is of course random linear algebra, and very close to a randomised matrix factorisation.
I am especially interested in seeing how this might be useful for dependent data, especially time series.
Brian McWilliams, Gabriel Krummenacher and Mario Lučić, Randomized Linear Regression: A brief overview and recent results.
Martin Wainright, Statistics meets Optimization: Randomization and approximation for high-dimensional problems.
In the modern era of high-dimensional data, the interface between mathematical statistics and optimization has become an increasingly vibrant area of research. In this course, we provide some vignettes into this interface, including the following topics:
- Dimensionality reduction via random projection. The naive idea of projecting high-dimensional data to a randomly chosen low-dimensional space is remarkably effective. We discuss the classical Johnson-Lindenstrauss lemma, as well as various modern variants that provide computationally-efficient embeddings with strong guarantees.
- When is it possible to quickly obtain approximate solutions of large-scale convex programs? In practice, methods based on randomized projection can work very well, and arguments based on convex analysis and concentration of measure provide a rigorous underpinning to these observations.
- Optimization problems with some form of nonconvexity arise frequently in statistical settings — for instance, in problems with latent variables, combinatorial constraints, or rank constraints. Nonconvex programs are known to be intractable in a complexity-theoretic sense, but the random ensembles arising in statistics are not adversarially constructed. Under what conditions is it possible to make rigorous guarantees about the behavior of simple iterative algorithms for such problems? We develop some general theory for addressing these questions, exploiting tools from both optimization theory and empirical process theory.
- Cover, T. M.(1965) Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition. IEEE Transactions on Electronic Computers, EC-14(3), 326–334. DOI.
- Dhillon, P., Lu, Y., Foster, D. P., & Ungar, L. (2013) New subsampling algorithms for fast least squares regression. In Advances in Neural Information Processing Systems (pp. 360–368). Curran Associates, Inc.
- Heinze, C., McWilliams, B., & Meinshausen, N. (2016) DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. (pp. 875–883). Presented at the Proceedings of the 19th International Conference on Artificial Intelligence and Statistics
- Heinze, C., McWilliams, B., Meinshausen, N., & Krummenacher, G. (2014) LOCO: Distributing Ridge Regression with Random Projections. arXiv:1406.3469 [Stat].
- Krummenacher, G., McWilliams, B., Kilcher, Y., Buhmann, J. M., & Meinshausen, N. (2016) Scalable Adaptive Stochastic Optimization Using Random Projections. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 29 (pp. 1750–1758). Curran Associates, Inc.
- McWilliams, B., Balduzzi, D., & Buhmann, J. M.(2013) Correlated random features for fast semi-supervised learning. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 26 (Vol. 1050, pp. 440–448). Curran Associates, Inc.
- McWilliams, B., Krummenacher, G., Lucic, M., & Buhmann, J. M.(2014) Fast and Robust Least Squares Estimation in Corrupted Linear Models. arXiv:1406.3175 [Stat].
- Scardapane, S., & Wang, D. (2017) Randomness in neural networks: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(2), n/a-n/a. DOI.
- Thanei, G.-A., Heinze, C., & Meinshausen, N. (2017) Random Projections For Large-Scale Regression. arXiv:1701.05325 [Math, Stat].
- Wang, H., Zhu, R., & Ma, P. (2017) Optimal Subsampling for Large Sample Logistic Regression. arXiv:1702.01166 [Stat].
- Zhang, X., Wang, L., & Gu, Q. (2017) Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements. arXiv:1701.00481 [Stat].