Principal Component Analysis: A Natural Approach to Data Exploration
Felipe L. Gewers,Gustavo Rodrigues Ferreira,Henrique Ferraz de Arruda,Filipi Nascimento Silva,Cesar H. Comin,Diego R. Amancio,Luciano da Fontoura Costa +6 more
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TLDR
In this paper, the potential of using PCA for dimensionality reduction is illustrated on several real-world datasets, and several theoretical and practical aspects of PCA are reported in an accessible and integrated manner.Abstract:
Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA. The basic principles underlying PCA, data standardization, possible visualizations of the PCA results, and outlier detection are subsequently addressed. Next, the potential of using PCA for dimensionality reduction is illustrated on several real-world datasets. Finally, we summarize PCA-related approaches and other dimensionality reduction techniques. All in all, the objective of this work is to assist researchers from the most diverse areas in using and interpreting PCA.read more
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Cluster analysis and display of genome-wide expression patterns
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Eigenfaces for recognition
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TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.