Develops multi-scale and alternating-diffusion fused variants of CKA and distance correlation via Markov matrices for neural representation comparison, reporting state-of-the-art results on ReSi and OOD benchmarks.
Latent common manifold learning with alternating diffusion: analysis and applications
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abstract
The analysis of data sets arising from multiple sensors has drawn significant research attention over the years. Traditional methods, including kernel-based methods, are typically incapable of capturing nonlinear geometric structures. We introduce a latent common manifold model underlying multiple sensor observations for the purpose of multimodal data fusion. A method based on alternating diffusion is presented and analyzed; we provide theoretical analysis of the method under the latent common manifold model. To exemplify the power of the proposed framework, experimental results in several applications are reported.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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From Layers to Networks: Comparing Neural Representations via Diffusion Geometry
Develops multi-scale and alternating-diffusion fused variants of CKA and distance correlation via Markov matrices for neural representation comparison, reporting state-of-the-art results on ReSi and OOD benchmarks.