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New research proposes DNN-NSR for matrix completion using nonsmooth regularization

By PulseAugur Editorial · [2 sources] ·

A new research paper on arXiv introduces a novel method for matrix completion using nonsmooth regularization in fully connected neural networks (FCNNs). The proposed algorithm, DNN-NSR, addresses the overfitting issue common in FCNNs by incorporating L1 norm regularization on intermediate representations and the nuclear norm of weight matrices. This approach results in a nonsmooth and nonconvex objective function, requiring a specialized proximal gradient method for optimization. The paper demonstrates that DNN-NSR outperforms existing linear and nonlinear algorithms in simulations. AI

IMPACT This research contributes to more robust and generalizable matrix completion techniques, potentially improving performance in applications relying on reconstructing incomplete data.

RANK_REASON The cluster contains two academic papers from arXiv discussing matrix completion algorithms.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research proposes DNN-NSR for matrix completion using nonsmooth regularization

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The cluster contains two academic papers from arXiv discussing matrix completion algorithms.
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67 days old
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sajad Faramarzi, Farzan Haddadi, Sajjad Amini, Masoud Ahookhosh, Symeon Chatzinotas ·

    Matrix Completion via Nonsmooth Regularization of Fully Connected Neural Networks

    arXiv:2403.10232v2 Announce Type: replace-cross Abstract: Conventional matrix completion methods approximate the missing values by assuming the matrix to be low-rank, which leads to a linear approximation of missing values. It has been shown that enhanced performance could be att…

  2. arXiv stat.ML TIER_1 English(EN) · Moritz Hardt ·

    Understanding Alternating Minimization for Matrix Completion

    arXiv:1312.0925v4 Announce Type: replace-cross Abstract: Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems. Theoretical guarantees for Alternating Minimization have been hard to come by …

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