mlsanityData to 7 Oct 2026

Lifelong Matrix Completion with Sparsity-Number

arXiv 2203.07637 ↗First posted 15 Mar 2022cs.LGcited by 4

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Abstract (arXiv)

Matrix completion problem has been previously studied under various adaptive and passive settings. Previously, researchers have proposed passive, two-phase and single-phase algorithms using coherence parameter, and multi phase algorithm using sparsity-number. It has been shown that the method using sparsity-number reaching to theoretical lower bounds in many conditions. However, the aforementioned method is running in many phases through the matrix completion process, therefore it makes much more informative decision at each stage. Hence, it is natural that the method outperforms previous algorithms. In this paper, we are using the idea of sparsity-number and propose and single-phase column space recovery algorithm which can be extended to two-phase exact matrix completion algorithm. Moreover, we show that these methods are as efficient as multi-phase matrix recovery algorithm. We provide experimental evidence to illustrate the performance of our algorithm.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 7 of its 16 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

7 of 16 references.

References · 7

  1. 2013Low-Rank Matrix and Tensor Completion via Adaptive Samplingcited by 19
  2. 2014Finding a sparse vector in a subspace: Linear sparsity using alternating directionscited by 18
  3. 2014On the Power of Adaptivity in Matrix Completion and Approximationcited by 7
  4. 2016Noise-Tolerant Life-Long Matrix Completion via Adaptive Samplingcited by 6
  5. 2020Optimal Exact Matrix Completion Under new Parametrizationcited by 5
  6. 2020Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasetscited by 4
  7. 2022Performance of Distribution Regression with Doubling Measure under the seek of Closest Pointcited by 2

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Citing papers per year: 2022: 4.

4 papers.