Ella Bingham,
Ella Bingham,
Samuel Kaski,
Samuel Kaski,
Jorma Laaksonen,
Jouko Lampinen,
Jouko Lampinen,
Jorma Laaksonen
e.a.
Advances in Independent Component Analysis and Learning Machines
Gebonden Engels 2015 9780128028063Levertijd ongeveer 9 werkdagen
Gratis verzonden
Samenvatting
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.
Examples of topics which have developed from the advances of ICA, which are covered in the book are:
A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithm Unsupervised deep learning Machine vision and image retrieval
Specificaties
ISBN13:9780128028063
Taal:Engels
Bindwijze:Gebonden
Aantal pagina's:328
Uitgever:Elsevier Science
Hoofdrubriek:Wetenschap en techniek, Computer en informatica
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
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
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
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