# An Adaptation for Iterative Structured Matrix Completion

@article{Kassab2020AnAF, title={An Adaptation for Iterative Structured Matrix Completion}, author={Lara Kassab and Henry Adams and Deanna Needell}, journal={2020 54th Asilomar Conference on Signals, Systems, and Computers}, year={2020}, pages={1451-1456} }

Matrix completion is the task of predicting missing entries of matrix from a subset of known entries. Notions of structured matrix completion include any setting in which whether an entry is observed does not occur uniformly at random. In recent work, a modification to the standard nuclear norm minimization for matrix completion has been made to take into account sparsity-based structure in the missing entries, which is motivated e.g. in recommender systems. In this work, we propose adjusting…

## 2 Citations

### Convex and Nonconvex Approaches for the Matrix Completion Problem

- Computer Science2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST)
- 2022

This paper test the (convex) soft-impute and (non-conveX) alternating proximal matrix completion methods using some real data and test [1]’s claim that non-conventus approaches perform better than convex approaches, and shows a comparison of the two approaches in terms of computational complexity, execution time, and prediction accuracy.

### Research Statement: Bridging applied and quantitative topology

- Mathematics
- 2022

Henry Adams, Colorado State University Large sets of high-dimensional data are common in most branches of science, and their shapes reflect important patterns within. The goal of topological data…

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