Presents the first Õ(log^{1.5} n)-approximation algorithm for the graph label selection problem under a standard budget constraint.
Proceedings of the Twentieth International Conference on International Conference on Machine Learning , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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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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An Approximation Algorithm for Graph Label Selection
Presents the first Õ(log^{1.5} n)-approximation algorithm for the graph label selection problem under a standard budget constraint.
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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.