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Found 17 papers, 1 papers with code
New Methods for MLE of Toeplitz Structured Covariance Matrices with Applications to RADAR Problems
This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix.
Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA
We also propose two important reformulations of the fair PCA problem: a) fair robust PCA -- which can handle outliers in the data, and b) fair sparse PCA -- which can enforce sparsity on the estimated fair principal components.
Pearson-Matthews correlation coefficients for binary and multinary classification and hypothesis testing
We also present an additional new metric for multinary classification which can be viewed as a direct extension of MCC.
Comments on "Iteratively Re-weighted Algorithm for Fuzzy c-Means"
In this comment, we present a simple alternate derivation to the IRW-FCM algorithm presented in "Iteratively Re-weighted Algorithm for Fuzzy c-Means" for Fuzzy c-Means problem.
Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks
This paper investigates the hybrid source localization problem using the four radio measurements - time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA).
Optimal Sensor Placement for Hybrid Source Localization Using Fused TOA-RSS-AOA Measurements
In this paper, we present an optimal sensor placement methodology, which is based on the principle of majorization-minimization (MM), for hybrid localization technique.
Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals
The proposed algorithm does not require tuning of any hyperparameter and it has the desirable feature of eliminating the inactive variables in the course of the iterations - which can help speeding up the algorithm.
New Methods for MLE of Toeplitz Structured Covariance Matrices with Applications to RADAR Problems
This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix.
PDMM: A novel Primal-Dual Majorization-Minimization algorithm for Poisson Phase-Retrieval problem
In this paper, we introduce a novel iterative algorithm for the problem of phase-retrieval where the measurements consist of only the magnitude of linear function of the unknown signal, and the noise in the measurements follow Poisson distribution.
Optimal Sensor Placement for Source Localization: A Unified ADMM Approach
Source localization plays a key role in many applications including radar, wireless and underwater communications.
UNIPOL: Unimodular sequence design via a separable iterative quartic polynomial optimization for active sensing systems
Sequences having better autocorrelation properties play a crucial role in enhancing the performance of active sensing systems.
Design of MIMO Radar Waveforms based on lp-Norm Criteria
Multiple-input multiple-output (MIMO) radars transmit a set of sequences that exhibit small cross-correlation sidelobes, to enhance sensing performance by separating them at the matched filter outputs.
Designing sequence set with minimal peak side-lobe level for applications in high resolution RADAR imaging
Constant modulus sequence set with low peak side-lobe level is a necessity for enhancing the performance of modern active sensing systems like Multiple Input Multiple Output (MIMO) RADARs.
A Fast Iterative Algorithm to design phase only sequences by minimizing the ISL metric
Unimodular/Phase only sequence having impulse like aperiodic auto-correlation function plays a central role in the applications of RADAR, SONAR, Cryptography, and Wireless (CDMA) Communication Systems.
Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation
In addition, we propose a method to improve the covariance estimation problem when its underlying eigenvectors are known to be sparse.
Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions
This paper considers the problem of robustly estimating a structured covariance matrix with an elliptical underlying distribution with known mean.
Sparse Generalized Eigenvalue Problem via Smooth Optimization
Then an algorithm is developed via iteratively majorizing the surrogate function by a quadratic separable function, which at each iteration reduces to a regular generalized eigenvalue problem.
