Low rank matrix completion and realization of graphs: results and problems
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917901412335616 |
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| author | Dzhenzher, S. Garaev, T. Nikitenko, O. Petukhov, A. Skopenkov, A. Voropaev, A. |
| author_facet | Dzhenzher, S. Garaev, T. Nikitenko, O. Petukhov, A. Skopenkov, A. Voropaev, A. |
| contents | The Netflix problem (from machine learning) asks the following. Given a ratings matrix in which each entry $(i,j)$ represents the rating of movie $j$ by customer $i$, if customer $i$ has watched movie $j$, and is otherwise missing, we would like to predict the remaining entries in order to make good recommendations to customers on what to watch next. The remaining entries are predicted so as to minimize the {\it rank} of the completed matrix.
In this survey we study a more general problem, in which instead of knowing specific matrix elements, we know linear relations on such elements. We describe applications of these results to embeddings of graphs in surfaces (more precisely, embeddings with rotation systems, and embeddings modulo 2). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13935 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low rank matrix completion and realization of graphs: results and problems Dzhenzher, S. Garaev, T. Nikitenko, O. Petukhov, A. Skopenkov, A. Voropaev, A. History and Overview Discrete Mathematics Machine Learning Combinatorics Geometric Topology 15-02, 15A83, 57-02, 57M15, 57Q35, 05C10 The Netflix problem (from machine learning) asks the following. Given a ratings matrix in which each entry $(i,j)$ represents the rating of movie $j$ by customer $i$, if customer $i$ has watched movie $j$, and is otherwise missing, we would like to predict the remaining entries in order to make good recommendations to customers on what to watch next. The remaining entries are predicted so as to minimize the {\it rank} of the completed matrix. In this survey we study a more general problem, in which instead of knowing specific matrix elements, we know linear relations on such elements. We describe applications of these results to embeddings of graphs in surfaces (more precisely, embeddings with rotation systems, and embeddings modulo 2). |
| title | Low rank matrix completion and realization of graphs: results and problems |
| topic | History and Overview Discrete Mathematics Machine Learning Combinatorics Geometric Topology 15-02, 15A83, 57-02, 57M15, 57Q35, 05C10 |
| url | https://arxiv.org/abs/2501.13935 |