No-rank Tensor Decomposition Using Metric Learning

• URL: http://arxiv.org/abs/2511.01816v1
• Date: Mon, 03 Nov 2025 18:21:53 GMT
• Title: No-rank Tensor Decomposition Using Metric Learning
• Authors: Maryam Bagherian,
• Abstract summary: This paper introduces a no-rank tensor decomposition framework grounded in metric learning.<n>We provide theoretical guarantees for the framework's convergence and establish bounds on its metric properties.<n>Our approach achieves superior performance with smaller training datasets compared to transformer-based methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tensor decomposition faces fundamental challenges in analyzing high-dimensional data, where traditional methods based on reconstruction and fixed-rank constraints often fail to capture semantically meaningful structures. This paper introduces a no-rank tensor decomposition framework grounded in metric learning, which replaces reconstruction objectives with a discriminative, similarity-based optimization. The proposed approach learns data-driven embeddings by optimizing a triplet loss with diversity and uniformity regularization, creating a feature space where distance directly reflects semantic similarity. We provide theoretical guarantees for the framework's convergence and establish bounds on its metric properties. Evaluations across diverse domains -- including face recognition (LFW, Olivetti), brain connectivity analysis (ABIDE), and simulated data (galaxy morphology, crystal structures) -- demonstrate that our method outperforms baseline techniques, including PCA, t-SNE, UMAP, and tensor decomposition baselines (CP and Tucker). Results show substantial improvements in clustering metrics (Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, Separation Ratio, Adjusted Rand Index, Normalized Mutual Information) and reveal a fundamental trade-off: while metric learning optimizes global class separation, it deliberately transforms local geometry to align with semantic relationships. Crucially, our approach achieves superior performance with smaller training datasets compared to transformer-based methods, offering an efficient alternative for domains with limited labeled data. This work establishes metric learning as a paradigm for tensor-based analysis, prioritizing semantic relevance over pixel-level fidelity while providing computational advantages in data-scarce scenarios.

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