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| 书名： | Principal Manifolds for Data Visualization and Dimension Reduction | |
|---|---|---|
| 作者： | Alexander N. Gorban,Balázs Kégl,Donald C. Wunsch,Andrei Y. Zinovyev | |
| 出版时间： | 2008-01-01 | |
| ISBN： | 9783540737490(P-ISBN) ，9783540737506(O-ISBN) | |
| 摘要： | ||
| 摘要： | ||
书目详情：
Developments and Applications of Nonlinear Principal Component Analysis – a ReviewNonlinear Principal Component Analysis: Neural Network Models and ApplicationsLearning Nonlinear Principal Manifolds by Self-Organising MapsElastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data VisualizationTopology-Preserving Mappings for Data VisualisationThe Iterative Extraction Approach to ClusteringRepresenting Complex Data Using Localized Principal Components with Application to Astronomical DataAuto-Associative Models, Nonlinear Principal Component Analysis, Manifolds and Projection PursuitBeyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic ComplexesDiffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering AlgorithmsOn Bounds for Diffusion, Discrepancy and Fill Distance MetricsGeometric Optimization Methods for the Analysis of Gene Expression DataDimensionality Reduction and Microarray DataPCA and K-Means Decipher Genome
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