sklearn_tutorial/04.1-Dimensionality-PCA.ipynb at master · jakevdp/sklearn_tutorial · GitHub

11 days ago by lena

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:

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11 days ago by lena

How to read PCA plots — What do you mean "heterogeneity"?

11 days ago by lena

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.

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visualization
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11 days ago by lena

Understanding Principal Component Analysis – Rishav Kumar – Medium

11 days ago by lena

Concise explanation about math/linear algebra behind PCA with eigenvalue decomposition

math
statistics
pca
11 days ago by lena

Explained Visually

august 2018 by lena

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
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Regression, PCA, Eigenvalues, Pi, Sine/Cosine, Markov chains, Probability

august 2018 by lena

Practical Guide to Cluster Analysis in R (book)

july 2018 by lena

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
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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.

july 2018 by lena

PCA : Interpretation Examples — Stats366 / Stats 166 Course Notes

july 2018 by lena

Short tutorial, nice examples with dudi.pca. Turtles and olympic data

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statistics
july 2018 by lena

PCA - Principal Component Analysis Essentials - Articles - STHDA

july 2018 by lena

detailed pca plot examples with factominer/factoextra

pca
r
statistics
july 2018 by lena

Bioconductor - pcaMethods

june 2018 by lena

PDF R Script Data with outliers

PDF R Script Introduction

PDF R Script Missing value imputation

PDF Reference Manual

pca
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PDF R Script Introduction

PDF R Script Missing value imputation

PDF Reference Manual

june 2018 by lena

PythonDataScienceHandbook/05.09-Principal-Component-Analysis.ipynb at master · jakevdp/PythonDataScienceHandbook

june 2018 by lena

Mostly examples about PCA for images: recognize faces after dimension reduction.

jupyter
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june 2018 by lena

Principal Component Analysis (PCA) - A.B. Dufour - course2.pdf

may 2018 by lena

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. "

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and prcomp deal with the individuals only. "

may 2018 by lena

PCA-2016.pages - pcaTutorial.pdf

may 2018 by lena

9 pages introduction with basic r-code and references

pca
statistics
may 2018 by lena

How Machines Make Predictions: Finding Correlations in Complex Data

may 2018 by lena

Not really about machine learning, explains correlation with cosine angles vectors linear algebra

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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

may 2018 by lena

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
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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...

may 2018 by lena

dimensionality reduction - Relationship between SVD and PCA. How to use SVD to perform PCA? - Cross Validated

may 2018 by lena

Comprehensive answer with links to other PCA-related answers

statistics
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tutorial
may 2018 by lena

Principal Component Analysis explained visually

may 2018 by lena

Very good interactive visual explanation

pca
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visualization
may 2018 by lena

Principal Component Analysis in R: prcomp vs princomp - Articles - STHDA

may 2018 by lena

With nice plots from factoextra package

statistics
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r
may 2018 by lena

Multivariate - ChrisBilder.com

may 2018 by lena

PCA, factor analysis, cluster analysis, prediction, logistic regression, nearest neighbour. Notes and examples

regression
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r
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tutorial
may 2018 by lena

A Tutorial on Principal Component Analysis - Jonathon Shlens - 2005

may 2018 by lena

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
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...

may 2018 by lena

PCA How To2 - pca.pdf

may 2018 by lena

Principal Components Analysis:

A How-To Manual for R

pca
r
statistics
A How-To Manual for R

may 2018 by lena

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