5 papers
A Compressive Classification Framework for High-Dimensional Data
Muhammad Naveed Tabassum, Esa Ollila
We propose a compressive classification framework for settings where the data dimensionality is significantly higher than the sample size. The proposed method, referred to as compr…
Simultaneous Signal Subspace Rank and Model Selection with an Application to Single-snapshot Source Localization
Muhammad Naveed Tabassum, Esa Ollila
This paper proposes a novel method for model selection in linear regression by utilizing the solution path of regularized least-squares (LS) approach (i.e., Lasso). This m…
Sequential adaptive elastic net approach for single-snapshot source localization
Muhammad Naveed Tabassum, Esa Ollila
This paper proposes efficient algorithms for accurate recovery of direction-of-arrival (DoA) of sources from single-snapshot measurements using compressed beamforming (CBF). In CBF…
Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
Muhammad Naveed Tabassum, Esa Ollila
We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is s…
Pathwise Least Angle Regression and a Significance Test for the Elastic Net
Muhammad Naveed Tabassum, Esa Ollila
Least angle regression (LARS) by Efron et al. (2004) is a novel method for constructing the piece-wise linear path of Lasso solutions. For several years, it remained also as the de…