Introduces SRTD and SRTD-lite to symmetrize topological divergences for neural representations and NTS as a rank-correlation-based metric bounded in [-1,1] for cross-scenario benchmarking.
The shape of data: Intrinsic distance for data distributions.arXiv preprint arXiv:1905.11141,
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Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis
Introduces SRTD and SRTD-lite to symmetrize topological divergences for neural representations and NTS as a rank-correlation-based metric bounded in [-1,1] for cross-scenario benchmarking.