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Found 4 papers, 2 papers with code
A Compressive Classification Framework for High-Dimensional Data
We propose a compressive classification framework for settings where the data dimensionality is significantly higher than the sample size.
Simultaneous Signal Subspace Rank and Model Selection with an Application to Single-snapshot Source Localization
This paper proposes a novel method for model selection in linear regression by utilizing the solution path of $\ell_1$ regularized least-squares (LS) approach (i. e., Lasso).
Methodology Complex Variables Optimization and Control Applications
Sequential adaptive elastic net approach for single-snapshot source localization
This paper proposes efficient algorithms for accurate recovery of direction-of-arrival (DoA) of sources from single-snapshot measurements using compressed beamforming (CBF).
Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets.
