Matrix completion with deterministic pattern - a geometric perspective
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Abstract (arXiv)
We consider the matrix completion problem with a deterministic pattern of observed entries. In this setting, we aim to answer the question: under what condition there will be (at least locally) unique solution to the matrix completion problem, i.e., the underlying true matrix is identifiable. We answer the question from a certain point of view and outline a geometric perspective. We give an algebraically verifiable sufficient condition, which we call the well-posedness condition, for the local uniqueness of MRMC solutions. We argue that this condition is necessary for local stability of MRMC solutions, and we show that the condition is generic using the characteristic rank. We also argue that the low-rank approximation approaches are more stable than MRMC and further propose a sequential statistical testing procedure to determine the "true" rank from observed entries. Finally, we provide numerical examples aimed at verifying validity of the presented theory.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2018.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 3.
7 papers.
- 2022Robust Matrix Completion with Heavy-tailed Noisecited by 4
- 2024Symmetric Matrix Completion with ReLU Samplingcited by 4
- 2023Matrix Completion from General Deterministic Sampling Patternscited by 2
- 2025Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theorycited by 2
- 2025Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descentcited by 1
- 2022Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patternscited by 0
- 2025Truncated Matrix Completion - An Empirical Studycited by 0
- 2025Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent28 Apr 2025
- 2025Truncated Matrix Completion - An Empirical Study14 Apr 2025
- 2025Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory26 Jan 2025
- 2024Symmetric Matrix Completion with ReLU Sampling9 Jun 2024
- 2023Matrix Completion from General Deterministic Sampling Patterns4 Jun 2023
- 2022Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns10 Jun 2022
- 2022Robust Matrix Completion with Heavy-tailed Noise9 Jun 2022