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Understanding PCA using Shiny and Stack Overflow data
RT : The video for my talk about understanding principal component analysis using Stack Overflow data is no…
rstudioconf  rstats  pca  principal-components  from twitter
17 days ago by tarakc02
What is Jalview ? | jalview.org
Jalview is a free program for multiple sequence alignment editing, visualisation and analysis. Use it to view and edit sequence alignments, analyse them with phylogenetic trees and principal components analysis (PCA) plots and explore molecular structures and annotation.
software  bioinformatics  opensource  dna  genomics  genetics  pca  phylogeny 
7 weeks ago by sprague
Principal Component Analysis
CS5240 Theoretical Foundations in Multimedia
Leow Wee Kheng
Department of Computer Science
School of Computing
National University of Singapore
9 weeks ago by hustwj
How Are Principal Component Analysis and Singular Value Decomposition Related?
Principal Component Analysis, or PCA, is a well-known and widely used technique applicable to a wide variety of applications such as dimensionality reduction, data compression, feature extraction, and visualization. The basic idea is to project a dataset from many correlated coordinates onto fewer uncorrelated coordinates called principal components while still retaining most of the variability present in the data.

Singular Value Decomposition, or SVD, is a computational method often employed to calculate principal components for a dataset. Using SVD to perform PCA is efficient and numerically robust. Moreover, the intimate relationship between them can guide our intuition about what PCA actually does and help us gain additional insights into this technique.

In this post, I will explicitly describe the mathematical relationship between SVD and PCA and highlight some benefits of doing so. If you have used these techniques in the past but aren’t sure how they work internally this article is for you. By the end you should have an understanding of the motivation for PCA and SVD, and hopefully a better intuition about how to effectively employ them.
pca  svd  math  linearalgebra 
11 weeks ago by euler
Statistics for Applications Chapter 9: Principal Component Analysis (PCA)
Prof. Philippe Rigollet

MIT Course Number
18.650 / 18.6501
11 weeks ago by hustwj

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