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To have a fair evaluation we draw 1000 such matrices, calculate the mean, the minimum distortion obtained

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stat.ML 1

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2024 1

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Optimization Can Learn Johnson Lindenstrauss Embeddings

stat.ML · 2024-12-10 · conditional · novelty 7.0

Optimizing a regularized failure-probability objective over Gaussian sampler parameters (mean and variance) yields a deterministic Johnson-Lindenstrauss embedding in polynomial time.

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  • Optimization Can Learn Johnson Lindenstrauss Embeddings stat.ML · 2024-12-10 · conditional · none · ref 4

    Optimizing a regularized failure-probability objective over Gaussian sampler parameters (mean and variance) yields a deterministic Johnson-Lindenstrauss embedding in polynomial time.