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lena : pca   38

sklearn_tutorial/04.1-Dimensionality-PCA.ipynb at master · jakevdp/sklearn_tutorial · GitHub
The dimensionality reduction might seem a bit abstract in two dimensions, but the projection and dimensionality reduction can be extremely useful when visualizing high-dimensional data. Let's take a quick look at the application of PCA to the digits data we looked at before:
pca  statistics  ml 
11 days ago by lena
How to read PCA plots — What do you mean "heterogeneity"?
To try to be concrete, we will consider 100 "genes", and throughout we will generate 600 "cells" from two "cell types". Different ways of generating these cell types will lead to different patterns in the PCA plot.
ml  visualization  pca  statistics 
11 days ago by lena
Understanding Principal Component Analysis – Rishav Kumar – Medium
Concise explanation about math/linear algebra behind PCA with eigenvalue decomposition
math  statistics  pca 
11 days ago by lena
Explained Visually
Explained Visually (EV) is an experiment in making hard ideas intuitive inspired the work of Bret Victor's Explorable Explanations.

Regression, PCA, Eigenvalues, Pi, Sine/Cosine, Markov chains, Probability
math  programming  statistics  probability  visualization  pca  matrix  markov 
august 2018 by lena
Practical Guide to Cluster Analysis in R (book)
Although there are several good books on unsupervised machine learning/clustering and related topics, we felt that many of them are either too high-level, theoretical or too advanced. Our goal was to write a practical guide to cluster analysis, elegant visualization and interpretation.

The main parts of the book include:

distance measures,
partitioning clustering,
hierarchical clustering,
cluster validation methods, as well as,
advanced clustering methods such as fuzzy clustering, density-based clustering and model-based clustering.
books  statistics  pca  r 
july 2018 by lena
PCA : Interpretation Examples — Stats366 / Stats 166 Course Notes
Short tutorial, nice examples with dudi.pca. Turtles and olympic data
pca  statistics 
july 2018 by lena
PCA - Principal Component Analysis Essentials - Articles - STHDA
detailed pca plot examples with factominer/factoextra
pca  r  statistics 
july 2018 by lena
Bioconductor - pcaMethods
PDF R Script Data with outliers
PDF R Script Introduction
PDF R Script Missing value imputation
PDF Reference Manual
pca  r  statistics 
june 2018 by lena
Principal Component Analysis (PCA) - A.B. Dufour - course2.pdf
Interesting tutorial, with 3D plots that explain effects of scaling/centering. Uses dudi.pca r code: "dudi.pca deals with the variables and/or the individuals whereas princomp
and prcomp deal with the individuals only. "
pca  r  statistics 
may 2018 by lena
PCA-2016.pages - pcaTutorial.pdf
9 pages introduction with basic r-code and references
pca  statistics 
may 2018 by lena
How Machines Make Predictions: Finding Correlations in Complex Data
Not really about machine learning, explains correlation with cosine angles vectors linear algebra
pca  correlation  statistics 
may 2018 by lena
PCA iteratively finds directions of greatest variance; but how to find a whole subspace with greatest variance? - Cross Validated
Independent component analysis (ICA) might offer an insightful alternative. ICA provides components that are not constrained to be orthogonal with each other; this means that for some purposes ICs might be more helpful than PCs.

Looking to answer the qualitative aspect of the OP's original question: What if I know I only want two principal components in order to visualize my data in 2-dimensions ? ICs can be rather helpful as ICA tries to minimize the mutual information among the projected dat...
pca  statistics 
may 2018 by lena
Multivariate -
PCA, factor analysis, cluster analysis, prediction, logistic regression, nearest neighbour. Notes and examples
regression  pca  r  statistics  tutorial 
may 2018 by lena
A Tutorial on Principal Component Analysis - Jonathon Shlens - 2005
Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that
is widely used but poorly understood. The goal of this paper is to dispel the magic behind this
black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works; furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA. This tutorial does not shy away from explaining the ideas in formally, nor does it shy aw...
pca  tutorial  statistics 
may 2018 by lena
PCA How To2 - pca.pdf
Principal Components Analysis:
A How-To Manual for R
pca  r  statistics 
may 2018 by lena

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