Auxiliary function approach to independent component analysis and independent vector analysis

N. Ono

doi:10.1117/12.2179859

11 June 2015 Auxiliary function approach to independent component analysis and independent vector analysis

N. Ono

Proceedings Volume 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII; 94960L (2015) https://doi.org/10.1117/12.2179859
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

In this paper, we review an auxiliary function approach to independent component analysis (ICA) and independent vector analysis (IVA). The derived algorithm consists of two alternative updates: 1) weighted covariance matrix update and 2) demixing matrix update, which include no tuning parameters such as a step size in the gradient descent method. The monotonic decrease of the objective function is guaranteed by the principle of the auxiliary function method. The experimental evaluation shows that the derived update rules yield faster convergence and better results than natural gradient updates. An efficient implementation on a mobile phone is also presented.

Citation Download Citation

N. Ono "Auxiliary function approach to independent component analysis and independent vector analysis", Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960L (11 June 2015); https://doi.org/10.1117/12.2179859

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