5 papers
The -PCA Framework: A Unified and Efficiency-Preserving Approach with Robust Variants
Hung Hung, Zhi-Yu Jou, Su-Yun Huang +1
Principal component analysis (PCA) is a fundamental tool in multivariate statistics, yet its sensitivity to outliers and limitations in distributed environments restrict its effect…
Minimum Copula Divergence for Robust Estimation
Shinto Eguchi, Shogo Kato
This paper introduces a robust estimation framework based solely on the copula function. We begin by introducing a family of divergence measures tailored for copulas, including the…
Information Geometry for Maximum Diversity Distributions
Shinto Eguchi
In recent years, biodiversity measures have gained prominence as essential tools for ecological and environmental assessments, particularly in the context of increasingly complex a…
A Generalized Mean Approach for Distributed-PCA
Zhi-Yu Jou, Su-Yun Huang, Hung Hung +1
Principal component analysis (PCA) is a widely used technique for dimension reduction. As datasets continue to grow in size, distributed-PCA (DPCA) has become an active research ar…
Minimum Gamma Divergence for Regression and Classification Problems
Shinto Eguchi
The book is structured into four main chapters. Chapter 1 introduces the foundational concepts of divergence measures, including the well-known Kullback-Leibler divergence and its…