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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2408.12169 |
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| _version_ | 1866910907844526080 |
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| author | Zhu, Jiangning Wang, Zheng Shen, Zhiyang Wei, Lai Tian, Fengyuan Liu, Mengchen Liu, Shixia |
| author_facet | Zhu, Jiangning Wang, Zheng Shen, Zhiyang Wei, Lai Tian, Fengyuan Liu, Mengchen Liu, Shixia |
| contents | Matrix reordering permutes the rows and columns of a matrix to reveal meaningful visual patterns, such as blocks that represent clusters. A comprehensive collection of matrices, along with a scoring method for measuring the quality of visual patterns in these matrices, contributes to building a benchmark. This benchmark is essential for selecting or designing suitable reordering algorithms for specific tasks. In this paper, we build a matrix reordering benchmark, ReorderBench, with the goal of evaluating and improving matrix reordering techniques. This is achieved by generating a large set of representative and diverse matrices and scoring these matrices with a convolution- and entropy-based method. Our benchmark contains 2,835,000 binary matrices and 5,670,000 continuous matrices, each featuring one of four visual patterns: block, off-diagonal block, star, or band. We demonstrate the usefulness of ReorderBench through three main applications in matrix reordering: 1) evaluating different reordering algorithms, 2) creating a unified scoring model to measure the visual patterns in any matrix, and 3) developing a deep learning model for matrix reordering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12169 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ReorderBench: A Benchmark for Matrix Reordering Zhu, Jiangning Wang, Zheng Shen, Zhiyang Wei, Lai Tian, Fengyuan Liu, Mengchen Liu, Shixia Human-Computer Interaction Matrix reordering permutes the rows and columns of a matrix to reveal meaningful visual patterns, such as blocks that represent clusters. A comprehensive collection of matrices, along with a scoring method for measuring the quality of visual patterns in these matrices, contributes to building a benchmark. This benchmark is essential for selecting or designing suitable reordering algorithms for specific tasks. In this paper, we build a matrix reordering benchmark, ReorderBench, with the goal of evaluating and improving matrix reordering techniques. This is achieved by generating a large set of representative and diverse matrices and scoring these matrices with a convolution- and entropy-based method. Our benchmark contains 2,835,000 binary matrices and 5,670,000 continuous matrices, each featuring one of four visual patterns: block, off-diagonal block, star, or band. We demonstrate the usefulness of ReorderBench through three main applications in matrix reordering: 1) evaluating different reordering algorithms, 2) creating a unified scoring model to measure the visual patterns in any matrix, and 3) developing a deep learning model for matrix reordering. |
| title | ReorderBench: A Benchmark for Matrix Reordering |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.12169 |