# 0.1 Principal component analysis (pca)

 Page 1 / 1
A brief discussion of PCA.

## Principal component analysis

PCA is essentially just SVD. The only difference is that we usually center the data first using some grand mean before doing SVD. There are three perspectives of views for PCA. Each of them gives different insight on what PCA does.

## Low-rank approximation

$\begin{array}{c}\underset{Z}{\mathrm{min}}\text{ }\frac{1}{2}||X-Z|{|}_{F}^{2}\\ subject\text{\hspace{0.17em}}to\text{ }rank\left(Z\right)\le K\end{array}$

where Frobenius norm is a matrix version of sums of squared. This gives the interpretation of dimension reduction. Solution to the problem is: $Z=\sum _{i=1}^{K}{U}_{k}{d}_{k}{V}_{K}^{T}$

We do lose some information when doing dimension reduction, but the majority of variance is explained in the lower-rank matrix (The eigenvalues give us information about how significant the eigenvector is. So we put the eigenvalues in the order of the magnitude of the eigenvectors, and discard the smallest several since the contribution of components along that particular eigenvector is less significant comparing that with a large eigenvalue). PCA guarantees the best rank-K approximation to X. The tuning parameter K can be either chosen by cross-validation or AIC/BIC. This property is useful for data visualization when the data is high dimensional.

## Matrix factorization

$\begin{array}{l}\underset{\begin{array}{l}U,D,\\ V\end{array}}{\mathrm{minimize}}\left\{\frac{1}{2}{‖X-UD{V}^{T}‖}_{F}^{2}\right\}\\ subject\text{ }to\text{ }\text{ }\text{ }{U}^{T}U=I\text{ },{V}^{T}V=I\text{ },D\in dia{g}^{+}\end{array}$

This gives the interpretation of pattern recognition. The first column of U gives the first major pattern in sample (row) space while the first column of V gives the first major pattern in feature space. This property is also useful in recommender systems (a lot of the popular algorithms in collaborative filtering like SVD++, bias-SVD etc. are based upon this “projection-to-find-major-pattern” idea).

## Covariance

$\begin{array}{l}\mathrm{max}\text{ }{V}_{K}^{T}{X}^{T}X{V}_{K}\\ subject\text{\hspace{0.17em}}to\text{ }{V}_{K}^{T}{V}_{K}=1,{V}_{K}^{T}{V}_{j}=0\end{array}$

${X}^{T}X$ here behaves like covariates for multivariate Gaussian. This is essentially an eigenvalue problem of covariance: and . Interpretation here is that we are maximizing the covariates in column and row space.

(Figure Credit: https://onlinecourses.science.psu.edu/stat857/node/35)

## The intuition behind pca

The intuition behind PCA is as follows: The First PC (Principal Component) finds the linear combinations of variables that correspond to the direction with maximal sample variance (the major pattern of the dataset, the most spread out direction). Succeeding PCs then goes on to find direction that gives highest variance under the constraint of it being orthogonal (uncorrelated) to preceding ones. Geometrically, what we are doing is basically a coordinate transformation – the newly formed axes correspond to the newly constructed linear combination of variables. The number of the newly formed coordinate axes (variables) is usually much lower than the number of axes (variables) in the original dataset, but it’s still explaining most of the variance present in the data.

## Another interesting insight

Another interesting insight on PCA is provided by considering its relationship to Ridge Regression (L2 penalty). The result given by Ridge Regression can be written like this:

$\stackrel{^}{Y}=X{\stackrel{^}{\beta }}^{r}=\sum _{j=1}^{p}{u}_{j}\frac{d{}_{j}{}^{2}}{{d}_{j}^{2}+\lambda }{u}_{j}{}^{T}y$

The term in the middle here, $\frac{d{}_{j}{}^{2}}{{d}_{j}^{2}+\lambda }$ , shrinks the singular values. For those major patterns with large singular values, lambda has little effect for shrinking; but for those with small singular values, lambda has huge effect to shrink them towards zero (not exactly zero, unlike lasso - L1 penalty, which does feature selection). This non-uniform shrinkage thus has a grouping effect. This is why Ridge Regression is often used when features are strongly correlated (it only captures orthogonal major pattern). PCA is really easy to implement - feed the data matrix(n*p) to the SVD command in Matlab, extract the PC loading(V) and PC score(U) vector and we will get the major pattern we want.

where we get a research paper on Nano chemistry....?
nanopartical of organic/inorganic / physical chemistry , pdf / thesis / review
Ali
what are the products of Nano chemistry?
There are lots of products of nano chemistry... Like nano coatings.....carbon fiber.. And lots of others..
learn
Even nanotechnology is pretty much all about chemistry... Its the chemistry on quantum or atomic level
learn
da
no nanotechnology is also a part of physics and maths it requires angle formulas and some pressure regarding concepts
Bhagvanji
hey
Giriraj
Preparation and Applications of Nanomaterial for Drug Delivery
revolt
da
Application of nanotechnology in medicine
what is variations in raman spectra for nanomaterials
ya I also want to know the raman spectra
Bhagvanji
I only see partial conversation and what's the question here!
what about nanotechnology for water purification
please someone correct me if I'm wrong but I think one can use nanoparticles, specially silver nanoparticles for water treatment.
Damian
yes that's correct
Professor
I think
Professor
Nasa has use it in the 60's, copper as water purification in the moon travel.
Alexandre
nanocopper obvius
Alexandre
what is the stm
is there industrial application of fullrenes. What is the method to prepare fullrene on large scale.?
Rafiq
industrial application...? mmm I think on the medical side as drug carrier, but you should go deeper on your research, I may be wrong
Damian
How we are making nano material?
what is a peer
What is meant by 'nano scale'?
What is STMs full form?
LITNING
scanning tunneling microscope
Sahil
how nano science is used for hydrophobicity
Santosh
Do u think that Graphene and Fullrene fiber can be used to make Air Plane body structure the lightest and strongest. Rafiq
Rafiq
what is differents between GO and RGO?
Mahi
what is simplest way to understand the applications of nano robots used to detect the cancer affected cell of human body.? How this robot is carried to required site of body cell.? what will be the carrier material and how can be detected that correct delivery of drug is done Rafiq
Rafiq
if virus is killing to make ARTIFICIAL DNA OF GRAPHENE FOR KILLED THE VIRUS .THIS IS OUR ASSUMPTION
Anam
analytical skills graphene is prepared to kill any type viruses .
Anam
Any one who tell me about Preparation and application of Nanomaterial for drug Delivery
Hafiz
what is Nano technology ?
write examples of Nano molecule?
Bob
The nanotechnology is as new science, to scale nanometric
brayan
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Damian
Is there any normative that regulates the use of silver nanoparticles?
what king of growth are you checking .?
Renato
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
why we need to study biomolecules, molecular biology in nanotechnology?
?
Kyle
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
why?
what school?
Kyle
biomolecules are e building blocks of every organics and inorganic materials.
Joe
how did you get the value of 2000N.What calculations are needed to arrive at it
Privacy Information Security Software Version 1.1a
Good
Got questions? Join the online conversation and get instant answers!