Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.
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Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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The anti-lexicographic SUS-anchor: a near-optimal k=1 sampling scheme
The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.