A Geometric View of SRC: Learning Representations for Stable Residual Inference

arXiv:2605.29673v1 Announce Type: new Abstract: Reconstruction-based inference assigns a class by comparing class-wise reconstruction residuals; Sparse Representation Classification (SRC) is a canonical instance whose reliability depends on the geometry of the learned representation. We adopt a strict training-inference separation: SRC is used only as a fixed test-time rule and is never differentiated, unrolled, or optimized during training. In a span-level idealization based on class-conditional spans and their associated projection residuals, we formalize residual-ordering stability through

Source: arXiv cs.LG — read the full report at the original publisher.

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