In this work we propose a new method for solving the blind source separation (BSS) problem using a support vector machine (SVM) workbench. Thus, we provide an introduction to SVM-ICA, a theoretical approach to unsupervised learning based on learning machines, which has frequently been proposed for classification and regression tasks. The key idea is to construct a Lagrange function from both the objective function and the corresponding constraints, by introducing a dual set of variables and solving the optimization problem. For this purpose we define a specific cost function and its derivative in terms of independence, i.e. inner products between the output and the objective function, transforming an unsupervised learning problem into a supervised learning machine task where optimization theory can be applied to develop effective algorithms. © Springer-Verlag 2004.
CITATION STYLE
Puntonet, C. G., Górriz, J. M., Salmerón, M., & Hornillo-Mellado, S. (2004). Theoretical method for solving BSS-ICA using SVM. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 256–262. https://doi.org/10.1007/978-3-540-30110-3_33
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