Natural gradient works efficiently in learning
Neural Computation
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Divergence function, duality, and convex analysis
Neural Computation
Information geometry of U-Boost and Bregman divergence
Neural Computation
Robust Gaussian graphical modeling
Journal of Multivariate Analysis
Robust Prewhitening for ICA by Minimizing β-Divergence and Its Application to FastICA
Neural Processing Letters
Integration of Stochastic Models by Minimizing α-Divergence
Neural Computation
Information theoretic aspects of fairness criteria in network resource allocation problems
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Sparse Super Symmetric Tensor Factorization
Neural Information Processing
Tutorial series on brain-inspired computing: part 6: geometrical structure of boosting algorithm
New Generation Computing
α-divergence is unique, belonging to both f-divergence and Bregman divergence classes
IEEE Transactions on Information Theory
Robust QTL analysis by minimum β-divergence method
International Journal of Data Mining and Bioinformatics
Aggregated information representation for technical analysis on stock market with csiszár divergence
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Divergence-based vector quantization
Neural Computation
Algorithms for nonnegative matrix factorization with the β-divergence
Neural Computation
Extended SMART algorithms for non-negative matrix factorization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Density estimation with minimization of U-divergence
Machine Learning
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Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on the β divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.