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

InTech, 2012, -230 p.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 energy, multi-sensor data fusion, materials science, gas chromatographic analysis, ecology, video and image processing, agriculture, color coating, climate and automatic target recognition. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.Principal Component Analysis – A Realization of Classification Success in Multi Sensor Data Fusion
Applications of Principal Component Analysis (PCA) in Materials Science
Methodology for Optimization of Polymer Blends Composition
Applications of PCA to the Monitoring of Hydrocarbon Content in Marine Sediments by Means of Gas Chromatographic Measurements
Application of Principal Component Analysis in Surface Water Quality Monitoring
EM-Based Mixture Models Applied to Video Event Detection
Principal Component Analysis in the Development of Optical and Imaging Spectroscopic Inspections for Agricultural / Food Safety and Quality
Application of Principal Components Regression for Analysis of X-Ray Diffraction Images of Wood
Principal Component Analysis in Industrial Colour Coating Formulations
Improving the Knowledge of Climatic Variability Patterns Using Spatio-Temporal Principal Component Analysis
Automatic Target Recognition Based on SAR Images and Two-Stage 2DPCA Features