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stat.ML2017★ 2 cited
Linear Dimensionality Reduction in Linear Time: Johnson-Lindenstrauss-type Guarantees for Random Subspace
Nick Lim, Robert J. Durrant
We consider the problem of efficient randomized dimensionality reduction with norm-preservation guarantees. Specifically we prove data-dependent Johnson-Lindenstrauss-type geometry…
stat.ML2017★ 1 cited
Maximum Margin Principal Components
Xianghui Luo, Robert J. Durrant
Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is…