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Advances in Independent Component Analysis and Learning Machines

Gebonden Engels 2015 9780128028063
€ 142,60
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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

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Inhoudsopgave

<p>Part 1: Methods<br>1. The Initial Convergence Rate of the FastICA Algorithm: The "One-Third Rule"<br>2. Improved variants of the FastICA algorithm<br>3. A unified probabilistic model for independent and principal component analysis<br>4. Riemannian optimization in complex-valued ICA<br>5. Non-Additive Optimization<br>6. Image denoising via local factor analysis under Bayesian Ying-Yang principle<br>7. Unsupervised Deep Learning: A Short Review<br>8. From Neural PCA to Deep Unsupervised Learning</p> <p>Part 2: Applications<br>9. Two Decades of Local Binary Patterns – A Survey<br>10. Subspace approach in Spectral Color Science<br>11. From pattern recognition methods to machine vision applications<br>12. Advances in Visual Concept Detection: Ten Years of TRECVID<br>13. On the applicability of latent variable modeling to research system data</p>

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€ 142,60
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        Advances in Independent Component Analysis and Learning Machines