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What is PCA

Handbook of Research in Mobile Business, Second Edition: Technical, Methodological and Social Perspectives
Principal Component Analysis
Published in Chapter:
Independent Component Analysis Algorithms in Wireless Communication Systems
Sargam Parmar (Ganpat University, India) and Bhuvan Unhelkar ( & University of Western & University of Western Sydney, Australia)
DOI: 10.4018/978-1-60566-156-8.ch043
In commercial cellular networks, like the systems based on direct sequence code division multiple access (DSCDMA), many types of interferences can appear, starting from multi-user interference inside each sector in a cell to interoperator interference. Also unintentional jamming can be present due to co-existing systems at the same band, whereas intentional jamming arises mainly in military applications. Independent Component Analysis (ICA) use as an advanced pre-processing tool for blind suppression of interfering signals in direct sequence spread spectrum communication systems utilizing antenna arrays. The role of ICA is to provide an interference-mitigated signal to the conventional detection. Several ICA algorithms exist for performing Blind Source Separation (BSS). ICA has been used to extract interference signals, but very less literature is available on the performance, that is, how does it behave in communication environment? This needs an evaluation of its performance in communication environment. This chapter evaluates the performance of some major ICA algorithms like Bell and Sejnowski’s infomax algorithm, Cardoso’s Joint Approximate Diagonalization of Eigen matrices (JADE), Pearson-ICA, and Comon’s algorithm in a communication blind source separation problem. Independent signals representing Sub-Gaussian, Super-Gaussian, and mix users, are generated and then mixed linearly to simulate communication signals. Separation performance of ICA algorithms is measured by performance index.
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Automated Image Analysis Approaches in Histopathology
Principal Component Analysis. A dimension reduction technique used to transform a feature set into a new feature set whose features map to the variance in the system. The new features that provide the least amount of variance can subsequently be removed.
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Analysis of Frequency Domain Data Generated by a Quartz Crystal
Principal components analysis is a method that reduces a high dimensional data into fewer dimensions, and in this process, the representative information is retained.
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Bioinspired Associative Memories
Principal component analysis is a technique used to reduce multidimensional data sets to lower dimensions for analysis. PCA involves the computation of the eigenvalue decomposition of a data set, usually after mean centering the data for each attribute.
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Statistical Analysis and Linear Modeling of the Heat Exchangers Fouling in Phosphoric Acid Concentration Units
A data analysis technique that allows, from n variables, to construct m other variables called principal components.
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Trends of ECG Analysis and Diagnosis
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (KLT), the Hotelling transform or proper orthogonal decomposition (POD).
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Feature Selection in Pathology Detection using Hybrid Multidimensional Analysis
Principal Component Analysis. Orthogonal representation based on data variance.
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Business and Industrial Applications of Machine Learning Algorithms
Principal component analysis that transforms data in sub-dimensional space.
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Biometrical Processing of Faces in Security and Forensics
PCA stands for Principal Component Analysis. Also known as Karhunen-Loeve Transform (KLT). This is popular in face recognition method of dimensionality reduction of data sets, based on eigen-decomposition of covariance matrix.
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A Primer on Q-Method and the Study of Technology
A standard, mathematically unambiguous factor extraction procedure that can be found in standard textbooks on factor analysis.
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In Machina Systems for the Rational De Novo Peptide Design
Principle Component Analysis. Technique seeking a projection which represents the data in a best way. The new coordinates can be considered as linear combinations of the original descriptor axes often treated as factors(principle components) ( Schneider, Baringhaus, 2008 ).
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Adaptive Principal Component Analysis-Based Outliers Detection Through Neighborhood Voting in Wireless Sensor Networks
Principle component analysis; in the context of this chapter, a technique used for lossless data compression and decompression.
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Comparison of Methods to Display Principal Component Analysis, Focusing on Biplots and the Selection of Biplot Axes
Stands for principal components analysis - a multivariate analysis applied to a matrix with no groups defined, that carries out data compression to the most relevant underlying factors
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