Preserving Vector Space Properties in Dimensionality Reduction: A Relationship Preserving Loss Framework
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912563526107136 |
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| author | Weinwurm, Eddi Kovalenko, Alexander |
| author_facet | Weinwurm, Eddi Kovalenko, Alexander |
| contents | Dimensionality reduction can distort vector space properties such as orthogonality and linear independence, which are critical for tasks including cross-modal retrieval, clustering, and classification. We propose a Relationship Preserving Loss (RPL), a loss function that preserves these properties by minimizing discrepancies between relationship matrices (e.g., Gram or cosine) of high-dimensional data and their low-dimensional embeddings. RPL trains neural networks for non-linear projections and is supported by error bounds derived from matrix perturbation theory. Initial experiments suggest that RPL reduces embedding dimensions while largely retaining performance on downstream tasks, likely due to its preservation of key vector space properties. While we describe here the use of RPL in dimensionality reduction, this loss can also be applied more broadly, for example to cross-domain alignment and transfer learning, knowledge distillation, fairness and invariance, dehubbing, graph and manifold learning, and federated learning, where distributed embeddings must remain geometrically consistent. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_01198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Preserving Vector Space Properties in Dimensionality Reduction: A Relationship Preserving Loss Framework Weinwurm, Eddi Kovalenko, Alexander Machine Learning Artificial Intelligence Dimensionality reduction can distort vector space properties such as orthogonality and linear independence, which are critical for tasks including cross-modal retrieval, clustering, and classification. We propose a Relationship Preserving Loss (RPL), a loss function that preserves these properties by minimizing discrepancies between relationship matrices (e.g., Gram or cosine) of high-dimensional data and their low-dimensional embeddings. RPL trains neural networks for non-linear projections and is supported by error bounds derived from matrix perturbation theory. Initial experiments suggest that RPL reduces embedding dimensions while largely retaining performance on downstream tasks, likely due to its preservation of key vector space properties. While we describe here the use of RPL in dimensionality reduction, this loss can also be applied more broadly, for example to cross-domain alignment and transfer learning, knowledge distillation, fairness and invariance, dehubbing, graph and manifold learning, and federated learning, where distributed embeddings must remain geometrically consistent. |
| title | Preserving Vector Space Properties in Dimensionality Reduction: A Relationship Preserving Loss Framework |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.01198 |