Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Oktober 2007
Genre:	Naturwissensch., Medizin, Technik
	Artificial Intelligence / C / Computational Intelligence / Computational Science and Engineering / Control and Systems Theory / Humanities / Humanities and Social Sciences / Interdisciplinary studies / Interdisziplinäre Studien / Künstliche Intelligenz / Mathematical Methods in Physics / Mathematical physics / Mathematics and Statistics / Mathematics—Data processing / Mathematische Physik / Numerical analysis / Numerische Mathematik / Probability and statistics / Science, Humanities and Social Sciences, multidisciplinary / Social Sciences / Statistics / Wahrscheinlichkeitsrechnung und Statistik mehr...
ISBN:	9783540737490
EAN-Code:	9783540737490
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 19 mm
Gewicht:	552 gr
Seiten:	340
Illustration:	XXIV, 340 p. 82 illus., 14 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.