# Robust Partial Least Squares Using Low Rank and Sparse Decomposition

arXiv: https://arxiv.org/abs/2407.06936 · HTML: https://mlsanity.com/paper/2407.06936/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2024.

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## Abstract (arXiv)

This paper proposes a framework for simultaneous dimensionality reduction and regression in the presence of outliers in data by applying low-rank and sparse matrix decomposition. For multivariate data corrupted with outliers, it is generally hard to estimate the true low dimensional manifold from corrupted data. The objective of the proposed framework is to find a robust estimate of the low dimensional space of data to reliably perform regression. The effectiveness of the proposed algorithm is demonstrated experimentally for simultaneous regression and dimensionality reduction in the presence of outliers in data.