Finding the Largest Low-Rank Clusters With Ky Fan $2$-$k$-Norm and $\ell

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Finding the Largest Low-Rank Clusters With Ky Fan $2$-$k$-Norm and $\ell_1$-Norm

Published:2016-01 Issue:1 Volume:26 Page:274-312
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Doan Xuan Vinh,Vavasis Stephen

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software

Link

http://epubs.siam.org/doi/pdf/10.1137/140962097

Reference16 articles.

1. Guaranteed clustering and biclustering via semidefinite programming

2. A Proximal Point Algorithm for Sequential Feature Extraction Applications

3. Finding Approximately Rank-One Submatrices with the Nuclear Norm and $\ell_1$-Norm

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3. Coded computing for on-device cooperative inference;Mobile Edge Artificial Intelligence;2022

4. Low-rank matrix recovery with Ky Fan 2-k-norm;Journal of Global Optimization;2021-06-02

5. $\ell_0$ -Motivated Low-Rank Sparse Subspace Clustering;IEEE Transactions on Cybernetics;2020-04

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