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Advances in Independent Component Analysis and Learning Machines
The very latest advances in independent component analysis and machine learning
Ella Bingham (Edited by), Samuel Kaski (Edited by), Jorma Laaksonen (Edited by), Jouko Lampinen (Edited by)
9780128028063
Hardback, published 15 April 2015
328 pages
23.4 x 19 x 2.4 cm, 0.75 kg
Approx.296 pages
Part 1: Methods 1. The Initial Convergence Rate of the FastICA Algorithm: The "One-Third Rule" 2. Improved variants of the FastICA algorithm 3. A unified probabilistic model for independent and principal component analysis 4. Riemannian optimization in complex-valued ICA 5. Non-Additive Optimization 6. Image denoising via local factor analysis under Bayesian Ying-Yang principle 7. Unsupervised Deep Learning: A Short Review 8. From Neural PCA to Deep Unsupervised Learning Part 2: Applications 9. Two Decades of Local Binary Patterns – A Survey 10. Subspace approach in Spectral Color Science 11. From pattern recognition methods to machine vision applications 12. Advances in Visual Concept Detection: Ten Years of TRECVID 13. On the applicability of latent variable modeling to research system data
Subject Areas: Signal processing [UYS], Machine learning [UYQM], Stochastics [PBWL]
