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arXiv 2018年

作者： Zenil, Hector Kiani, Narsis A. Adams, Alyssa Abrahão, Felipe S. Rueda-Toicen, Antonio Zea, Allan A. Tegnér, Jesper King’s College London School of Biomedical Engineering and Imaging Sciences United Kingdom The Alan Turing Institute British Library United Kingdom Oxford Immune Algorithmics United Kingdom Oxford Immune Algorithmics Abu Dhabi United Arab Emirates Oxford Immune Algorithmics Dubai United Arab Emirates Karolinska Institutet Algorithmic Dynamics Lab Sweden Algorithmic Nature Group LABORES for the Natural and Digital Sciences France Cross Labs Cross Compass Japan Karolinska Institutet Department of Oncology-Pathology Center of Molecular Medicine Stockholm Sweden University of Wisconsin-Madison John W. and Jeanne M. Rowe Center for Research in Virology Morgridge Institute for Research United States Brazil Data Extreme Lab Brazil Universidad Central de Venezuela Instituto Nacional de Bioingeniería Venezuela Universidad Central de Venezuela Department of Mathematics Venezuela Biological and Environmental Science and Engineering Division Saudi Arabia

We present a novel, domain-agnostic, model-independent, unsupervised, and universally applicable approach for data summarization. Specifically, we focus on addressing the challenge of reducing certain dimensionality aspects, such as the number of edges in a network, while retaining essential features of interest. These features include preserving crucial network properties like degree distribution, clustering coefficient, edge betweenness, and degree and eigenvector centralities. Our approach outperforms state-of-the-art network reduction techniques by achieving an average improvement in feature preservation. Previous methods grounded in statistics or classical information theory have been limited in their ability to capture more intricate patterns and features, particularly nonlinear patterns stemming from deterministic computable processes. Moreover, these approaches heavily rely on a priori feature selection, demanding constant supervision. Our findings demonstrate the effectiveness of the algorithms proposed in this study in overcoming these limitations, all while maintaining a time-efficient computational profile. In many instances, our approach not only matches but also surpasses the performance of established network reduction algorithms. Furthermore, we extend the applicability of our method to lossy compression tasks involving images or any bi-dimensional data. This highlights the versatility and broad utility of our approach in various domains. Copyright © 2018, The Authors. All rights reserved.

关键词： Dimensionality reduction

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Methods and Applications of algorithmic Complexity 1

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丛书名： Emergence, Complexity and Computation

1000年

作者： Hector Zenil Fernando Soler Toscano Nicolas Gauvrit

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