Sanguansat P. (ed.) Principal Component Analysis - Multidisciplinary Applications

Rijeka: InTech, 2012. — 200 p. — ISBN: 978-953-51-0129-1.It is more than a century since Karl Pearson invented the concept of Principal Component Analysis (PCA). Nowadays, it is a very useful tool in data analysis in many fields. PCA is the technique of dimensionality reduction, which transforms data in the high-dimensional space to space of lower dimensions. The advantages of this subspace are numerous. First of all, the reduced dimension has the effect of retaining the most of the useful information while reducing noise and other undesirable artifacts. Secondly, the time and memory that used in data processing are smaller. Thirdly, it provides a way to understand and visualize the structure of complex data sets. Furthermore, it helps us identify new meaningful underlying variables.
Indeed, PCA itself does not reduce the dimension of the data set. It only rotates the axes of data space along lines of maximum variance. The axis of the greatest variance is called the first principal component. Another axis, which is orthogonal to the previous one and positioned to represent the next greatest variance, is called the second principal component, and so on. The dimension reduction is done by using only the first few principal components as a basis set for the new space. Therefore, this subspace tends to be small and may be dropped with minimal loss of information.
Originally, PCA is the orthogonal transformation which can deal with linear data. However, the real-world data is usually nonlinear and some of it, especially multimedia data, is multilinear. Recently, PCA is not limited to only linear transformation. There are many extension methods to make possible nonlinear and multilinear transformations via manifolds based, kernel-based and tensor-based techniques. This generalization makes PCA more useful for a wider range of applications.
In this book the reader will find the applications of PCA in many fields such as taxonomy, biology, pharmacy, finance, agriculture, ecology, health, architecture. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.Kernel Methods for Dimensionality Reduction Applied to the «Omics» Data
Principal Component Analysis in the Era of «Omics» Data
Chemometrics (PCA) in Pharmaceutics: Tablet Development, Manufacturing and Quality Assurance
Pharmacophoric Profile: Design of New Potential Drugs with PCA Analysis
Empirical Study: Do Fund Managers Herd to Counter Investor Sentiment?
Application of the Principal Component Analysis to Disclose Factors Influencing on the Composition of Fungal Consortia Deteriorating Remained Fruit Stalks on Sour Cherry Trees
Application of PCA in Taxonomy Research – Thrips (Insecta, Thysanoptera) as a Model Group
PCA – A Powerful Method for Analyze Ecological Niches
The Health Care Access Index as a Determinant of Delayed Cancer Detection Through Principal Component Analysis
Principal Component Analysis Applied to SPECT and PET Data of Dementia Patients – A Review
Public Parks Aesthetic Value Index