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High Dimensional Data Analysis

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High Dimensional Data Analysis

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Research Frontiers in Sparse Principal Component Analysis Methods

Development of PCA variants that enforce sparsity constraints to identify interpretable principal components in high-dimensional datasets.

Interpretability in Ultra-High Dimensional Feature Selection
Sparsity-Inducing Priors in Nonlinear Manifold Learning
Adaptive Regularization Across Heterogeneous Data Regimes
Computational Scalability in Billion-Dimensional Sparse Decomposition
Stability and Reproducibility of Sparse Component Extraction
Multi-View Sparse Dimensionality Reduction Under Missingness
Dynamic Sparsity Patterns in Temporal High-Dimensional Systems
Robust Sparse PCA Under Heavy-Tailed Distributions
Information-Theoretic Limits of Sparse Subspace Recovery
Causal Structure Learning via Sparse Component Analysis

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