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Principal Manifolds for Data Visualization and Dimension Reduction

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Cover of 'Principal Manifolds for Data Visualization and Dimension Reduction'

Table of Contents

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    Book Overview
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    Chapter 1 Developments and Applications of Nonlinear Principal Component Analysis – a Review
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    Chapter 2 Nonlinear Principal Component Analysis: Neural Network Models and Applications
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    Chapter 3 Learning Nonlinear Principal Manifolds by Self-Organising Maps
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    Chapter 4 Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization
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    Chapter 5 Topology-Preserving Mappings for Data Visualisation
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    Chapter 6 The Iterative Extraction Approach to Clustering
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    Chapter 7 Representing Complex Data Using Localized Principal Components with Application to Astronomical Data
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    Chapter 8 Auto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection Pursuit
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    Chapter 9 Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes
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    Chapter 10 Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms
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    Chapter 11 On Bounds for Diffusion, Discrepancy and Fill Distance Metrics
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    Chapter 12 Geometric Optimization Methods for the Analysis of Gene Expression Data
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    Chapter 13 Dimensionality Reduction and Microarray Data
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    Chapter 14 PCA and K-Means Decipher Genome
Attention for Chapter 10: Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms
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