384 search results for "PCA"

PCA for NIR Spectra_part 001: "Plotting the loadings"

February 22, 2012
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PCA for NIR Spectra_part 001: "Plotting the loadings"

There are different algorithms to calculate the Principal Components (PCs). Kurt Varmuza & Peter Filzmozer explain  them in their book: “Introduction to Multivariate Statistical Analysis in Chemometrics”.I´m going to apply one of them, to...

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Modelling returns using PCA : Evidence from Indian equity market

December 26, 2011
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As my finance term paper, I investigated an interesting question where I tried to identify macroeconomic variables that explain the returns on equities. Much of the debate has already taken place on this topic which has given rise to two competing theo...

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PCA file calculation with "R".

December 5, 2011
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PCA file calculation with "R".

X es la matriz centrada (X is the centered matrix). Xcov es la matriz de covarianzas de X (Xcov is the covariance matrix of X).Con la función "eigen" calculamos los "eigenvectors" y "eigenvalues" de Xcov.(With the function "eigen" we calculate the "ei...

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Big-Data PCA: 50 years of stock data

June 17, 2011
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Big-Data PCA: 50 years of stock data

In this post, Revolution engineer Sherry LaMonica shows us how to use the RevoScaleR big-data package in Revolution R Enterprise to do principal components analysis on 50 years of stock market data -- ed. Principal components analysis, or PCA, seeks to find a set of orthogonal axes such that the first axis, or first principal component, accounts for as...

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Principal Component Analysis (PCA) vs Ordinary Least Squares (OLS): A Visual Explanation

September 16, 2010
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Principal Component Analysis (PCA) vs Ordinary Least Squares (OLS): A Visual Explanation

Over at stats.stackexchange.com recently, a really interesting question was raised about principal component analysis (PCA). The gist was “Thanks to my college class I can do the math, but what does it MEAN?” I felt like this a number of times in my life. Many of my classes were focused on the technical implementations they kinda

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Using R and r.mapcalc (GRASS) to Estimate Mean Topographic Curvature

August 3, 2010
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Using R and r.mapcalc (GRASS) to Estimate Mean Topographic Curvature

Recently I was re-reading a paper on predictive soil mapping (Park et al, 2001), and considered testing one of their proposed terrain attributes in GRASS. The attribute, originally described by Blaszczynski (1997), is the distance-weighted mean differe...

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Tutorial: Principal Components Analysis (PCA) in R

May 20, 2010
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Found this tutorial by Emily Mankin on how to do principal components analysis (PCA) using R. Has a nice example with R code and several good references. The example starts by doing the PCA manually, then uses R's built in prcomp() function to do the s...

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Compcache on Ubuntu on Amazon EC2

May 4, 2010
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Compcache on Ubuntu on Amazon EC2

The following fully-automatic Bash script downloads, compiles, and initializes compcache version 0.6.2 on Ubuntu Karmic Koala (9.10). This script creates two swaps with a maximum of 4GB uncompressed size each. Two swaps are used to take advantage of 2 CPUs (or CPU cores in a multicore CPU). Compcache is a fascinating memory compression system. The

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Image Compression with Principal Component Analysis

January 26, 2017
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Image Compression with Principal Component Analysis

Image compression with principal component analysis is a frequently occurring application of the dimension reduction technique. Recall from a previous post that employed singular value decomposition to compress an image, that an image is a matrix of pixels represented by RGB color values. Thus, principal component analysis can be used... The post Image Compression with Principal Component Analysis...

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Principal Component Analysis in R

January 23, 2017
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Principal Component Analysis in R

Principal component analysis (PCA) is routinely employed on a wide range of problems. From the detection of outliers to predictive modeling, PCA has the ability of projecting the observations described by variables into few orthogonal components defined at where the data ‘stretch’ the most, rendering a simplified overview. PCA is particularly powerful in dealing with multicollinearity and variables that … Continue...

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