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[2103.14203] Deep Two-Way Matrix Reordering for Relational Data Analysis
Summary
The paper presents Deep Two-Way Matrix Reordering (DeepTMR), a novel method for matrix reordering that utilizes neural networks to extract features and visualize structural patterns in relational data.
Why It Matters
This research addresses the limitations of traditional matrix reordering techniques, which often rely on predefined feature extraction. By employing a neural network, DeepTMR can adaptively identify patterns, enhancing data analysis in various fields such as machine learning and data science.
Key Takeaways
- DeepTMR uses neural networks for effective matrix reordering.
- It automatically extracts nonlinear features from observed matrices.
- The method outputs a denoised mean matrix for better visualization.
- Demonstrated effectiveness on both synthetic and practical datasets.
- Addresses challenges in identifying structural patterns without prior knowledge.
Statistics > Machine Learning arXiv:2103.14203 (stat) [Submitted on 26 Mar 2021 (v1), last revised 16 Feb 2026 (this version, v5)] Title:Deep Two-Way Matrix Reordering for Relational Data Analysis Authors:Chihiro Watanabe, Taiji Suzuki View a PDF of the paper titled Deep Two-Way Matrix Reordering for Relational Data Analysis, by Chihiro Watanabe and 1 other authors View PDF HTML (experimental) Abstract:Matrix reordering is a task to permute the rows and columns of a given observed matrix such that the resulting reordered matrix shows meaningful or interpretable structural patterns. Most existing matrix reordering techniques share the common processes of extracting some feature representations from an observed matrix in a predefined manner, and applying matrix reordering based on it. However, in some practical cases, we do not always have prior knowledge about the structural pattern of an observed matrix. To address this problem, we propose a new matrix reordering method, called deep two-way matrix reordering (DeepTMR), using a neural network model. The trained network can automatically extract nonlinear row/column features from an observed matrix, which can then be used for matrix reordering. Moreover, the proposed DeepTMR provides the denoised mean matrix of a given observed matrix as an output of the trained network. This denoised mean matrix can be used to visualize the global structure of the reordered observed matrix. We demonstrate the effectiveness of the proposed D...
