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MMA: Multi-Metric-Autoencoder for Analyzing High-Dimensional and Incomplete Data

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14173))

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Abstract

High-dimensional and incomplete (HDI) data usually arise in various complex applications, e.g., bioinformatics and recommender systems, making them commonly heterogeneous and inclusive. Deep neural networks (DNNs)-based approaches have provided state-of-the-art representation learning performance on HDI data. However, most prior studies adopt fixed and exclusive \(L_2\)-norm-oriented loss and regularization terms. Such a single-metric-oriented model yields limited performance on heterogeneous and inclusive HDI data. Motivated by this, we propose a Multi-Metric-Autoencoder (MMA) whose main ideas are two-fold: 1) employing different \(L_p\)-norms to build four variant Autoencoders, each of which resides in a unique metric representation space with different loss and regularization terms, and 2) aggregating these Autoencoders with a tailored, self-adaptive weighting strategy. Theoretical analysis guarantees that our MMA could attain a better representation from a set of dispersed metric spaces. Extensive experiments on four real-world datasets demonstrate that our MMA significantly outperforms seven state-of-the-art models. Our code is available at the link https://github.com/wudi1989/MMA/

This work is supported by the National Natural Science Foundation of China under grant 62176070.

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Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, 510006, China

    Cheng Liang & Teng Huang

  2. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Di Wu & Xin Luo

  3. Department of Computer Science, Old Dominion University, Norfolk, VA, 23529, USA

    Yi He

  4. Department of Computer Science, Xavier University of Louisiana, New Orleans, LA, 70125, USA

    Zhong Chen

Authors
  1. Cheng Liang
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  2. Di Wu
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  4. Teng Huang
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  5. Zhong Chen
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  6. Xin Luo
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Corresponding author

Correspondence to Di Wu .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Liang, C., Wu, D., He, Y., Huang, T., Chen, Z., Luo, X. (2023). MMA: Multi-Metric-Autoencoder for Analyzing High-Dimensional and Incomplete Data. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14173. Springer, Cham. https://doi.org/10.1007/978-3-031-43424-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-43424-2_1

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