GTSA-PCA replaces global PCA covariance with curvature-weighted local operators and a geodesic alignment step to produce geometry-aware embeddings that improve on standard PCA and UMAP in small-sample high-curvature settings.
ISPRS International Journal of Geo-Information , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A framework applies frequent itemset mining with the negFIN algorithm and unsupervised learning to identify cities sharing co-occurring land use patterns from Copernicus Urban Atlas data.
citing papers explorer
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Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning
GTSA-PCA replaces global PCA covariance with curvature-weighted local operators and a geodesic alignment step to produce geometry-aware embeddings that improve on standard PCA and UMAP in small-sample high-curvature settings.
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Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning
A framework applies frequent itemset mining with the negFIN algorithm and unsupervised learning to identify cities sharing co-occurring land use patterns from Copernicus Urban Atlas data.