MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization

[2410.19504] MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2410.19504 (cs) [Submitted on 25 Oct 2024 ( v1 ), last revised 16 Aug 2026 (this version, v3)] Title: MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization Authors: Zelin Zang , Yuhao Wang , Jinlin Wu , Hong Liu , Yue Shen , Zhen Lei , Stan Z. Li View a PDF of the paper titled MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization, by Zelin Zang and 6 other authors View PDF HTML (experimental) Abstract: Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential information. However, achieving both high DR accuracy and strong explainability remains a fundamental challenge, especially for users dealing with high-dimensional data. Traditional DR methods often face a trade-off between precision and transparency, where optimizing for performance can lead to reduced explainability, and vice versa. This limitation is especially prominent in real-world applications such as image, tabular, and text data analysis, where both accuracy and explainability are critical. To address these challenges, this work introduces the MoE-based Explainable Deep Manifold Transformation (DMT-ME). The proposed approach combines a geometry-aware hyperbolic mapper with Mixture of Experts (MoE) models, where sparse expert specialization provides the main representational gain and the hyperbolic component offers an additional refinement for structurally complex data. DMT-ME enhances DR accuracy primarily through MoE-based sparse routing and structure-aware matching, while also improving explainability by explicitly linking input data, embedding outcomes, and key features through the MoE structure. Extensive experiments demonstrate that DMT-ME consistently achieves superior performance in both DR accuracy and model explainability, making it a robust solution for complex data analysis. The code is available at this https URL . Comments: 17 pages, 15 figures, accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2410.19504 [cs.LG] (or arXiv:2410.19504v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2410.19504 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026 Related DOI : https://doi.org/10.1109/TPAMI.2026.3705888 Focus to learn more DOI(s) linking to related resources Submission history From: Zelin Zang [ view email ] [v1] Fri, 25 Oct 2024 12:11:32 UTC (43,749 KB) [v2] Sun, 29 Jun 2025 13:59:31 UTC (39,443 KB) [v3] Sun, 16 Aug 2026 09:23:39 UTC (13,599 KB) Full-text links: Access Paper: View a PDF of the paper titled MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization, by Zelin Zang and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-10 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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