Characterization and detection of noise in clustering
Pattern Recognition Letters
Signal Processing - Signal processing with heavy-tailed models
Robust techniques for wireless communications in non-gaussian environments
Robust techniques for wireless communications in non-gaussian environments
Nonlinear Signal and Image Processing: Theory, Methods, and Applications
Nonlinear Signal and Image Processing: Theory, Methods, and Applications
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistically-efficient filtering in impulsive environments: weighted myriad filters
EURASIP Journal on Applied Signal Processing
Myriad-Type Polynomial Filtering
IEEE Transactions on Signal Processing
Meridian Filtering for Robust Signal Processing
IEEE Transactions on Signal Processing
Hybrid Polynomial Filters for Gaussian and Non-Gaussian Noise Environments
IEEE Transactions on Signal Processing
Optimality of the myriad filter in practical impulsive-noiseenvironments
IEEE Transactions on Signal Processing
The stability test for symmetric alpha-stable distributions
IEEE Transactions on Signal Processing
A general weighted median filter structure admitting negativeweights
IEEE Transactions on Signal Processing
Polynomial weighted median filtering
IEEE Transactions on Signal Processing
Constrained Decentralized Estimation Over Noisy Channels for Sensor Networks
IEEE Transactions on Signal Processing
Blind decentralized estimation for bandwidth constrained wireless sensor networks
IEEE Transactions on Wireless Communications - Part 1
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Highly Robust Error Correction byConvex Programming
IEEE Transactions on Information Theory
Impulsive noise cancelation with simplified Cauchy-based p-norm filter
Signal Processing
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Statistical modeling is at the heart of many engineering problems. The importance of statistical modeling emanates not only from the desire to accurately characterize stochastic events, but also from the fact that distributions are the central models utilized to derive sample processing theories and methods. The generalized Cauchy distribution (GCD) family has a closed-form pdf expression across the whole family as well as algebraic tails, whichmakes it suitable formodeling many real-life impulsive processes. This paper develops a GCD theory-based approach that allows challenging problems to be formulated in a robust fashion. Notably, the proposed framework subsumes generalized Gaussian distribution (GGD) family-based developments, thereby guaranteeing performance improvements over traditional GCD-based problem formulation techniques. This robust framework can be adapted to a variety of applications in signal processing. As examples, we formulate four practical applications under this framework: (1) filtering for power line communications, (2) estimation in sensor networks with noisy channels, (3) reconstruction methods for compressed sensing, and (4) fuzzy clustering.