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High Dimensional Data Analysis

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High Dimensional Data Analysis

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Research Frontiers in Random Projection and Johnson-Lindenstrauss Lemma

Study of computationally efficient dimensionality reduction through random projections while preserving distances and geometric properties.

Geometry Preservation Under Extreme Dimensionality Reduction
Random Projection Beyond Euclidean Spaces and Metrics
Adaptive Sketching in Streaming High-Dimensional Data
Sparse Random Projections for Large-Scale Learning
Quantum-Enhanced Random Projections and Complexity Limits
Non-Gaussian Projections and Tail Probability Concentration
Layered Projection Schemes in Deep Neural Architectures
Data-Dependent Bounds Beyond Johnson-Lindenstrauss Framework
Random Projections in Distributional Robustness and Adversarial Settings
Optimality and Information-Theoretic Limits of Dimensionality Reduction

All High Dimensional Data Analysis PhD categories