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20142025
most citedOptimization Methods for Designing Sequences with Low Autocorrelation Sidelobes

390 citations · 1.5k across the 27 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

eess.SP2023★ 20 cited

New Methods for MLE of Toeplitz Structured Covariance Matrices with Applications to RADAR Problems

Augusto Aubry, Prabhu Babu, Antonio De Maio +1

This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix. In this regard, an equivalent reformulation of the MLE problem is introduced and…

stat.ME2023

Two new algorithms for maximum likelihood estimation of sparse covariance matrices with applications to graphical modeling

Ghania Fatima, Prabhu Babu, Petre Stoica

In this paper, we propose two new algorithms for maximum-likelihood estimation (MLE) of high dimensional sparse covariance matrices. Unlike most of the state of-the-art methods, wh…

eess.SP2023★ 1 cited

Pearson-Matthews correlation coefficients for binary and multinary classification and hypothesis testing

Petre Stoica, Prabhu Babu

The Pearson-Matthews correlation coefficient (usually abbreviated MCC) is considered to be one of the most useful metrics for the performance of a binary classification or hypothes…

stat.ML2023

Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA

Prabhu Babu, Petre Stoica

In this paper we propose a new iterative algorithm to solve the fair PCA (FPCA) problem. We start with the max-min fair PCA formulation originally proposed in [1] and derive a simp…

stat.ME2023★ 16 cited

Low-rank covariance matrix estimation for factor analysis in anisotropic noise: application to array processing and portfolio selection

Petre Stoica, Prabhu Babu

Factor analysis (FA) or principal component analysis (PCA) models the covariance matrix of the observed data as R = SS' + Σ, where SS' is the low-rank covariance matrix of the fact…