Deep Two-Way Matrix Reordering for Relational Data Analysis
This addresses the need for interpretable data analysis in relational data, but it is incremental as it builds on existing matrix reordering techniques with a neural network approach.
The paper tackles the problem of matrix reordering without prior knowledge of structural patterns by proposing DeepTMR, a neural network-based method that automatically extracts nonlinear features and provides a denoised mean matrix for visualization, showing effectiveness on synthetic and practical datasets.
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 DeepTMR by applying it to both synthetic and practical datasets.