Principal Manifolds for Data Visualization and Dimension ReductionAlexander N. Gorban, Balázs Kégl, Donald C. Wunsch, Andrei Zinovyev Springer Science & Business Media, 2007. gada 11. sept. - 340 lappuses In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and K-means decipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics. |
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1.–5. rezultāts no 89.
... for details see Fig. 1 This explanation sounds very modern: in “many cases of physics. p2 ,...p n from a line AB. Then we shall make1 1 Developments and Applications of Nonlinear Principal Component Analysis –. VI Preface.
... application field is much wider. It is useful for adaptive coding and data binning, and is a model reduction method, as well as the PCA: the PCA allows us to substitute a high-dimensional vector by its projection on a best fitted ...
... applications attended this workshop. The first chapter of the book presents a general review of existing NLPCA algorithms (U. Kruger, J. Zhang, and L. Xie). Next, M. Scholz, M. Fraunholz, and J. Selbig focus attention on autoassociative ...
... applications inspire development of new approaches to data approximation. In many chapters biological applications play central role. For the comparison of various algorithms, several test datasets were selected and presented to the ...
... Applications Matthias Scholz, Martin Fraunholz, and Joachim Selbig ............... 44 2.1 Introduction ... Applications of Nonlinear Principal Component Analysis – a Review Fig. 1.1. Principle of nonlinearity test.
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References | 39 |
References | 65 |
References | 91 |
References | 127 |
The Iterative Extraction Approach to Clustering | 151 |
References | 174 |
Components | 192 |
Principal Trees | 219 |
of Bacterial Genomes | 229 |
Diffusion Maps a Probabilistic Interpretation for Spectral | 238 |
On Bounds for Diffusion Discrepancy and Fill Distance | 261 |
References | 269 |
Dimensionality Reduction and Microarray Data | 293 |
References | 307 |
PCA and KMeans Decipher Genome | 309 |
Citi izdevumi - Skatīt visu
Principal Manifolds for Data Visualization and Dimension Reduction Alexander N. Gorban,Balázs Kégl,Donald C. Wunsch,Andrei Zinovyev Ierobežota priekšskatīšana - 2007 |
Principal Manifolds for Data Visualization and Dimension Reduction Alexander N. Gorban,Balázs Kégl,Donald C. Wunsch,Andrei Zinovyev Priekšskatījums nav pieejams - 2009 |