# Efficient Computation of Sparse and Robust Maximum Association Estimators

arXiv: https://arxiv.org/abs/2311.17563 · HTML: https://mlsanity.com/paper/2311.17563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [The Minimum Regularized Covariance Determinant estimator](https://mlsanity.com/paper/1701.07086/) (2017)
- [Sparse CCA via Precision Adjusted Iterative Thresholding](https://mlsanity.com/paper/1311.6186/) (2013)
- [Sparse canonical correlation analysis from a predictive point of view](https://mlsanity.com/paper/1501.01231/) (2015)
- [Robust Sparse Canonical Correlation Analysis](https://mlsanity.com/paper/1501.01233/) (2015)
- [Spatially smoothed robust covariance estimation for local outlier detection](https://mlsanity.com/paper/2305.05371/) (2023)

## Abstract (arXiv)

Robust statistical estimators offer resilience against outliers but are often computationally challenging, particularly in high-dimensional sparse settings. Modern optimization techniques are utilized for robust sparse association estimators without imposing constraints on the covariance structure. The approach splits the problem into a robust estimation phase, followed by optimization of a decoupled, biconvex problem to derive the sparse canonical vectors. An augmented Lagrangian algorithm, combined with a modified adaptive gradient descent method, induces sparsity through simultaneous updates of both canonical vectors. Results demonstrate improved precision over existing methods, with high-dimensional empirical examples illustrating the effectiveness of this approach. The methodology can also be extended to other robust sparse estimators.