Principal Manifolds for Data Visualization and Dimension Reduction 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Wunsch, Donald C. 編作者

譯者

出版日期

364 頁數

$ 145.77 價格

叢書系列

9783540737490 圖書編碼

Principal Manifolds for Data Visualization and Dimension Reduction 在線電子書圖書標籤: 統計復雜係統原始/圖形思維

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發表於2024-07-03

Principal Manifolds for Data Visualization and Dimension Reduction 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Principal Manifolds for Data Visualization and Dimension Reduction 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Principal Manifolds for Data Visualization and Dimension Reduction 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Principal Manifolds for Data Visualization and Dimension Reduction 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Principal Manifolds for Data Visualization and Dimension Reduction 在線電子書圖書描述

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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