Subspace learning of neural networks. (2010)

• Record Type: Book
• Further Information: Note: Jian Cheng Lv, Zhang Yi, Jiliu Zhou.
• Other Names: Lv, Jian Cheng; Yi, Zhang; Zhou, Jiliu
• Contents: Preface Chapter 1. Introduction 1.1 Introduction 1.1.1 Linear Neural Networks 1.1.2 Subspace Learning 1.2 Subspace Learning Algorithms 1.2.1 PCA Learning Algorithms 1.2.2 MCA Learning Algorithms 1.2.3 ICA Learning Algorithms 1.3 Methods for Convergence Analysis 1.3.1 SDT Method 1.3.2 DCT Method 1.3.3 DDT Method 1.4 Block Algorithms 1.5 Simulation Data Set and Notation 1.6 Conclusions Chapter 2. PCA Learning Algorithms with Constants Learning Rates 2.1 Oja’s PCA Learning Algorithms 2.1.1 The Algorithms 2.1.2 Convergence Issue 2.2 Invariant Sets 2.2.1 Properties of Invariant Sets 2.2.2 Conditions for Invariant Sets 2.3 Convergence analysis via DDT Method 2.3.1 Problem Formulation 2.3.2 Proof of Convergence 2.4 Convergence analysis of Xu’s LMSER algorithm 2.5 Discussions 2.5.1 Learning Rates Selection 2.5.2 Initial Points Selection 2.6 Conclusions Chapter 3. PCA Learning Algorithms with Adaptive Learning Rates 3.1 Introduction 3.2 Adaptive Learning Rates 3.3 Oja’s Algorithm with Adaptive Learning Rates 3.4 Convergence Analysis of Oja’s Algorithm with Adaptive Learning Rates 3.4.1 Boundedness 3.4.2 Global Convergence 3.5 Simulations and Discussions 3.6 Conclusions Chapter 4. GHA PCA Learning Algorithms 4.1 GHA PCA Learning Alogrithms 4.1.1 The Algorithms 4.1.2 Convergence Issue 4.2 Problem Formulation 4.3 Convergence Analysis via DDT Method 4.3.1 Outline of Proof 4.3.2 Detail of Proof 4.4 Discussions and Simulations 4.4.1 Example 1 4.4.2 Example 2 4.4.3 Example 3 4.5 Conclusions Chapter 5. MCA Learning Algorithms 5.1 MCA Learning Algorithms 5.1.1 The Algorithms 5.1.2 Convergence Issue 5.2 Invariant Sets 5.2.1 Properties of Invariant Sets 5.2.2 Conditions for Invariant Sets 5.3 Convergence Analysis via DDT Methods 5.3.1 Problem Formulation 5.3.2 Proof of Convergence 5.4 Simulations and Discussions 5.5 Conclusions Chapter 6. ICA Learning Algorithms 6.1 Hyvarinen-Oja Algorithm 6.1.1 The Algorithm 6.1.2 Convergence Issue 6.2 Invariant Sets 6.2.1 Properties of Invariant Sets 6.2.2 Conditions for Invariant Sets 6.3 Convergence Analysis via DDT Method 6.3.1 Problem Formulation 6.3.2 Proof of Convergence 6.4 Simulations and Discussions 6.5 Conclusions Chapter 7. Chaotic Behaviors Arising from Learning Algorithms 7.1 Introduction to Chaotic Behaviors 7.1.1 Chaotic Behaviors 7.1.2 Lyapunov Exponents 7.2 Chaotic Behaviors Arising from PCA Learning Algorithms 7.2.1 Computing of Lyapunov Exponents 7.2.2 Simulation Results 7.3 Chaotic Behaviors Arising from MCA Learning Algorithms 7.3.1 Computing of Lyapunov Exponents 7.3.2 Simulation Results 7.4 Chaotic Behaviors Arising from ICA Learning Algorithms 7.4.1 Computing of Lyapunov Exponents 7.4.2 Simulation Results 7.5 Conclusions Chapter 8. Multi-Block-Based MCA for Nonlinear Surface Fitting 8.1 Introduction 8.2 MCA Neural Network for Nonlinear Surface Fitting 8.3 Multi-Block-Based MCA 8.4 Multi-Block-Based MCA for Nonlinear Surface Fitting 8.5 Conclusions Chapter 9. A ICA Algorithm for Extracting Fetal Electrocardiogram 9.1 Introduction 9.2 Problem Formulation 9.3 The Proposed Algorithm 9.4 Extracting Fetal Electrocardiogram 9.5 Conclusions Chapter 10. Some Applications of PCA Neural Networks 10.1 Introduction 10.2 Rigid Medical Image Registration 10.2.1 Introduction 10.2.2 Method 10.2.3 Simulation 10.2.4 Conclusions 10.3 A Chaotic Encryption System Based on PCA Algorithm 10.3.1 Chaos and Encryption 10.3.2 A Chaotic Encryption System 10.3.3 Simulation 10.3.4 Conclusion 10.4 Conclusion
• Publisher Details: Place of publication not identified : CRC Press
• Publication Date: 2010
• Extent: 1 online resource, illustrations
• Subjects: 006.32; Neural networks (Computer science); Computer algorithms
• Languages: English
• ISBNs: 9781439815366; 1439815364
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.