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Found 6 papers, 1 papers with code
Robust Principal Component Analysis using Density Power Divergence
On the other end of the spectrum, robust PCA algorithms solving principal component pursuit or similar optimization problems have high breakdown, but lack theoretical richness and demand high computational power compared to the M-estimators.
rSVDdpd: A Robust Scalable Video Surveillance Background Modelling Algorithm
In this paper, we present a new video surveillance background modelling algorithm based on a new robust singular value decomposition technique rSVDdpd which takes care of both these issues.
The extended Bregman divergence and parametric estimation
Minimization of suitable statistical distances~(between the data and model densities) has proved to be a very useful technique in the field of robust inference.
Statistics Theory Statistics Theory
Robust Inference Using the Exponential-Polynomial Divergence
Density-based minimum divergence procedures represent popular techniques in parametric statistical inference.
Density Estimation
Methodology
Robust Estimation under Linear Mixed Models: The Minimum Density Power Divergence Approach
Many real-life data sets can be analyzed using Linear Mixed Models (LMMs).
Methodology Applications 62F35, 62J05
A Novel Minimum Divergence Approach to Robust Speaker Identification
In this work, a novel solution to the speaker identification problem is proposed through minimization of statistical divergences between the probability distribution (g).
