We address multi-reference visual place recognition (VPR), where reference sets captured under varying conditions are used to improve localization performance. While deep learning with large-scale training improves robustness, increasing data diversity and model complexity incur extensive computational cost during training and deployment. Descriptor-level fusion via voting or aggregation avoids training, but often targets multi-sensor setups or relies on heuristics with limited gains under appearance and viewpoint change. We propose a training-free, descriptor-agnostic approach that jointly models places using multiple reference descriptors via matrix decomposition into basis representations, enabling projection-based residual matching. We also introduce SotonMV, a benchmark for multi-viewpoint VPR captured over time. Our method improves Recall@1 by up to $\approx$18% over conventional single-reference set matching on appearance-change datasets, and matches or outperforms existing multi-reference baselines across most appearance-change settings. For viewpoint-change datasets, our method supports matching when viewpoint is distributed among the references, with the benefit depending on reference set coverage of the query conditions. On unconstrained reference sets, we additionally observe gains of $\approx$5%, demonstrating strong generalization while remaining computationally lightweight.

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