Principal component analysis

"KLT" redirects here. For the Kanade–Lucas–Tomasi feature tracker used in computer vision, see Kanade–Lucas–Tomasi Feature Tracker.

PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction.

Principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (K.L.T.), the Hotelling transform or proper orthogonal decomposition (POD).

PCA was invented in 1901 by Karl Pearson. Now it is mostly used as a tool in exploratory data analysis and for making predictive models. PCA involves the calculation of the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute. The results of a PCA are usually discussed in terms of component scores and loadings (Shaw, 2003).

PCA is the simplest of the true eigenvector-based multivariate analyses. Often, its operation can be thought of as revealing the internal structure of the data in a way which best explains the variance in the data. If a multivariate dataset is visualised as a set of coordinates in a high-dimensional data space (1 axis per variable), PCA supplies the user with a lower-dimensional picture, a "shadow" of this object when viewed from its (in some sense) most informative viewpoint.

PCA is closely related to factor analysis; indeed, some statistical packages deliberately conflate the two techniques. True factor analysis makes different assumptions about the underlying structure and solves eigenvectors of a slightly different matrix.

Details

PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. PCA is theoretically the optimum transform for given data in least square terms.

For a data matrix, X^T, with zero empirical mean (the empirical mean of the distribution has been subtracted from the data set), where each row represents a different repetition of the experiment, and each column gives the results from a particular probe, the PCA transformation is given by:

$\mathbf {Y} ^{\rm {T}}=\mathbf {X} ^{\rm {T}}\mathbf {W}$

$=\mathbf {V} \mathbf {\Sigma }$

where the matrix Σ is an m-by-n diagonal matrix with nonnegative real numbers on the diagonal and W Σ V^T is the singular value decomposition (svd) of X.

Given a set of points in Euclidean space, the first principal component (the eigenvector with the largest eigenvalue) corresponds to a line that passes through the mean and minimizes sum squared error with those points. The second principal component corresponds to the same concept after all correlation with the first principal component has been subtracted out from the points. Each eigenvalue indicates the portion of the variance that is correlated with each eigenvector. Thus, the sum of all the eigenvalues is equal to the sum squared distance of the points with their mean divided by the number of dimensions. PCA essentially rotates the set of points around their mean in order to align with the first few principal components. This moves as much of the variance as possible (using a linear transformation) into the first few dimensions. The values in the remaining dimensions, therefore, tend to be highly correlated and may be dropped with minimal loss of information. PCA is often used in this manner for dimensionality reduction. PCA has the distinction of being the optimal linear transformation for keeping the subspace that has largest variance. This advantage, however, comes at the price of greater computational requirement if compared, for example, to the discrete cosine transform. Nonlinear dimensionality reduction techniques tend to be more computationally demanding than PCA.

Discussion

Though most derivations and implementations fail to identify the importance of mean subtraction, data centering is carried out because it is part of the solution towards finding a basis that minimizes the mean square error of approximating the data. Assuming zero empirical mean (the empirical mean of the distribution has been subtracted from the data set), the principal component w₁ of a data set x can be defined as:

$\mathbf {w} _{1}={\underset {\Vert \mathbf {w} \Vert =1}{\operatorname {\arg \,max} }}\,\operatorname {Var} \{\mathbf {w} ^{T}\mathbf {x} \}={\underset {\Vert \mathbf {w} \Vert =1}{\operatorname {\arg \,max} }}\,E\left\{\left(\mathbf {w} ^{T}\mathbf {x} \right)^{2}\right\}$

(See arg max for the notation.) With the first k − 1 components, the kth component can be found by subtracting the first $k-1$ principal components from x:

$\mathbf {\hat {x}} _{k-1}=\mathbf {x} -\sum _{i=1}^{k-1}\mathbf {w} _{i}\mathbf {w} _{i}^{T}\mathbf {x}$

and by substituting this as the new data set to find a principal component in

$\mathbf {w} _{k}={\underset {\Vert \mathbf {w} \Vert =1}{\operatorname {arg\,max} }}\,E\left\{\left(\mathbf {w} ^{T}\mathbf {\hat {x}} _{k-1}\right)^{2}\right\}.$

The Karhunen–Loève transform is therefore equivalent to finding the singular value decomposition of the data matrix X,

$\mathbf {X} =\mathbf {W} \mathbf {\Sigma } \mathbf {V} ^{T},$

and then obtaining the reduced-space data matrix Y by projecting X down into the reduced space defined by only the first L singular vectors, W_L:

$\mathbf {Y} =\mathbf {W_{L}} ^{T}\mathbf {X} =\mathbf {\Sigma _{L}} \mathbf {V_{L}} ^{T}$

The matrix W of singular vectors of X is equivalently the matrix W of eigenvectors of the matrix of observed covariances C = X X^T,

$\mathbf {X} \mathbf {X} ^{T}=\mathbf {W} \mathbf {\Sigma } \mathbf {\Sigma } ^{T}\mathbf {W} ^{T}$

The eigenvectors with the largest eigenvalues correspond to the dimensions that have the strongest correlation in the data set (see Rayleigh quotient).

PCA is equivalent to empirical orthogonal functions (EOF), a name which is used in meteorology.

An autoencoder neural network with a linear hidden layer is similar to PCA. Upon convergence, the weight vectors of the K neurons in the hidden layer will form a basis for the space spanned by the first K principal components. Unlike PCA, this technique will not necessarily produce orthogonal vectors.

PCA is a popular technique in pattern recognition. But it is not optimized for class separability. An alternative is the linear discriminant analysis, which does take this into account.

Table of symbols and abbreviations

Symbol	Meaning	Dimensions	Indices
$\mathbf {X} =\{X[m,n]\}$	data matrix, consisting of the set of all data vectors, one vector per column	$M\times N$	$m=1\ldots M$ $n=1\ldots N$
$N\,$	the number of column vectors in the data set	$1\times 1$	scalar
$M\,$	the number of elements in each column vector (dimension)	$1\times 1$	scalar
$L\,$	the number of dimensions in the dimensionally reduced subspace, $1\leq L\leq M$	$1\times 1$	scalar
$\mathbf {u} =\{u[m]\}$	vector of empirical means, one mean for each row m of the data matrix	$M\times 1$	$m=1\ldots M$
$\mathbf {s} =\{s[m]\}$	vector of empirical standard deviations, one standard deviation for each row m of the data matrix	$M\times 1$	$m=1\ldots M$
$\mathbf {h} =\{h[n]\}$	vector of all 1's	$1\times N$	$n=1\ldots N$
$\mathbf {B} =\{B[m,n]\}$	deviations from the mean of each row m of the data matrix	$M\times N$	$m=1\ldots M$ $n=1\ldots N$
$\mathbf {Z} =\{Z[m,n]\}$	z-scores, computed using the mean and standard deviation for each row m of the data matrix	$M\times N$	$m=1\ldots M$ $n=1\ldots N$
$\mathbf {C} =\{C[p,q]\}$	covariance matrix	$M\times M$	$p=1\ldots M$ $q=1\ldots M$
$\mathbf {R} =\{R[p,q]\}$	correlation matrix	$M\times M$	$p=1\ldots M$ $q=1\ldots M$
$\mathbf {V} =\{V[p,q]\}$	matrix consisting of the set of all eigenvectors of C, one eigenvector per column	$M\times M$	$p=1\ldots M$ $q=1\ldots M$
$\mathbf {D} =\{D[p,q]\}$	diagonal matrix consisting of the set of all eigenvalues of C along its principal diagonal, and 0 for all other elements	$M\times M$	$p=1\ldots M$ $q=1\ldots M$
$\mathbf {W} =\{W[p,q]\}$	matrix of basis vectors, one vector per column, where each basis vector is one of the eigenvectors of C, and where the vectors in W are a sub-set of those in V	$M\times L$	$p=1\ldots M$ $q=1\ldots L$
$\mathbf {Y} =\{Y[m,n]\}$	matrix consisting of N column vectors, where each vector is the projection of the corresponding data vector from matrix X onto the basis vectors contained in the columns of matrix W.	$L\times N$	$m=1\ldots L$ $n=1\ldots N$

Properties and limitations of PCA

PCA is theoretically the optimal linear scheme, in terms of least mean square error, for compressing a set of high dimensional vectors into a set of lower dimensional vectors and then reconstructing the original set. It is a non-parametric analysis and the answer is unique and independent of any hypothesis about data probability distribution. However, the latter two properties are regarded as weakness as well as strength, in that being non-parametric, no prior knowledge can be incorporated and that PCA compressions often incur loss of information.

The applicability of PCA is limited by the assumptions made in its derivation. These assumptions are:

• Assumption on linearity

We assumed the observed data set to be linear combinations of certain basis. Non-linear methods such as kernel PCA have been developed without assuming linearity.

• Assumption on the statistical importance of mean and covariance

PCA uses the eigenvectors of the covariance matrix and it only finds the independent axes of the data under the Gaussian assumption. For non-Gaussian or multi-modal Gaussian data, PCA simply de-correlates the axes. When PCA is used for clustering, its main limitation is that it does not account for class separability since it makes no use of the class label of the feature vector. There is no guarantee that the directions of maximum variance will contain good features for discrimination.

• Assumption that large variances have important dynamics

PCA simply performs a coordinate rotation that aligns the transformed axes with the directions of maximum variance. It is only when we believe that the observed data has a high signal-to-noise ratio that the principal components with larger variance correspond to interesting dynamics and lower ones correspond to noise.

Essentially, PCA involves only rotation and scaling. The above assumptions are made in order to simplify the algebraic computation on the data set. Some other methods have been developed without one or more of these assumptions; these are described below.

Computing PCA using the covariance method

Following is a detailed description of PCA using the covariance method (see also here). The goal is to transform a given data set X of dimension M to an alternative data set Y of smaller dimension L. Equivalently, we are seeking to find the matrix Y, where Y is the Karhunen–Loeve transform (KLT) of matrix X:

$\mathbf {Y} =\mathbb {KLT} \{\mathbf {X} \}$

Organize the data set

Suppose you have data comprising a set of observations of M variables, and you want to reduce the data so that each observation can be described with only L variables, L < M. Suppose further, that the data are arranged as a set of N data vectors $\mathbf {x} _{1}\ldots \mathbf {x} _{N}$ with each $\mathbf {x} _{n}$ representing a single grouped observation of the M variables.

• Write $\mathbf {x} _{1}\ldots \mathbf {x} _{N}$ as column vectors, each of which has M rows.
• Place the column vectors into a single matrix X of dimensions M × N.

Calculate the empirical mean

• Find the empirical mean along each dimension m = 1, ..., M.
• Place the calculated mean values into an empirical mean vector u of dimensions M × 1.

$u[m]={1 \over N}\sum _{n=1}^{N}X[m,n]$

Calculate the deviations from the mean

Mean subtraction is an integral part of the solution towards finding a principal component basis that minimizes the mean square error of approximating the data. Hence we proceed by centering the data as follows:

• Subtract the empirical mean vector u from each column of the data matrix X.
• Store mean-subtracted data in the M × N matrix B.

$\mathbf {B} =\mathbf {X} -\mathbf {u} \mathbf {h}$

where h is a 1 x N row vector of all 1's:

$h[n]=1\,\qquad \qquad \mathrm {for\ } n=1\ldots N$

Find the covariance matrix

• Find the M × M empirical covariance matrix C from the outer product of matrix B with itself:

$\mathbf {C} =\mathbb {E} \left[\mathbf {B} \otimes \mathbf {B} \right]=\mathbb {E} \left[\mathbf {B} \cdot \mathbf {B} ^{*}\right]={1 \over N}\sum _{}\mathbf {B} \cdot \mathbf {B} ^{*}$

where

$\mathbb {E}$ is the expected value operator,

$\otimes$ is the outer product operator, and

$*\$ is the conjugate transpose operator. Note that if B consists entirely of real numbers, which is the case in many applications, the "conjugate transpose" is the same as the regular transpose.

• Please note that the information in this section is indeed a bit fuzzy. See the covariance matrix sections on the discussion page for more information.

Find the eigenvectors and eigenvalues of the covariance matrix

• Compute the matrix V of eigenvectors which diagonalizes the covariance matrix C:

$\mathbf {V} ^{-1}\mathbf {C} \mathbf {V} =\mathbf {D}$

where D is the diagonal matrix of eigenvalues of C. This step will typically involve the use of a computer-based algorithm for computing eigenvectors and eigenvalues. These algorithms are readily available as sub-components of most matrix algebra systems, such as MATLAB, Mathematica, SciPy, IDL(Interactive Data Language), or GNU Octave as well as OpenCV.

• Matrix D will take the form of an M × M diagonal matrix, where

$D[p,q]=\lambda _{m}\qquad \mathrm {for} \qquad p=q=m$

is the mth eigenvalue of the covariance matrix C, and

$D[p,q]=0\qquad \mathrm {for} \qquad p\neq q.$

• Matrix V, also of dimension M × M, contains M column vectors, each of length M, which represent the M eigenvectors of the covariance matrix C.
• The eigenvalues and eigenvectors are ordered and paired. The mth eigenvalue corresponds to the mth eigenvector.

Rearrange the eigenvectors and eigenvalues

• Sort the columns of the eigenvector matrix V and eigenvalue matrix D in order of decreasing eigenvalue.
• Make sure to maintain the correct pairings between the columns in each matrix.

Compute the cumulative energy content for each eigenvector

• The eigenvalues represent the distribution of the source data's energy among each of the eigenvectors, where the eigenvectors form a basis for the data. The cumulative energy content g for the mth eigenvector is the sum of the energy content across all of the eigenvalues from 1 through m:

$g[m]=\sum _{q=1}^{m}D[q,q]\qquad \mathrm {for} \qquad m=1,\dots ,M$

Select a subset of the eigenvectors as basis vectors

• Save the first L columns of V as the M × L matrix W:

$W[p,q]=V[p,q]\qquad \mathrm {for} \qquad p=1,\dots ,M\qquad q=1,\dots ,L$

where

$1\leq L\leq M.$

• Use the vector g as a guide in choosing an appropriate value for L. The goal is to choose as small a value of L as possible while achieving a reasonably high value of g on a percentage basis. For example, you may want to choose L so that the cumulative energy g is above a certain threshold, like 90 percent. In this case, choose the smallest value of L such that

$g[m=L]\geq 90\%\,$

Convert the source data to z-scores

• Create an M × 1 empirical standard deviation vector s from the square root of each element along the main diagonal of the covariance matrix C:

$\mathbf {s} =\{s[m]\}={\sqrt {C[p,q]}}\qquad \mathrm {for\ } p=q=m=1\ldots M$

• Calculate the M × N z-score matrix:

$\mathbf {Z} ={\mathbf {B} \over \mathbf {s} \cdot \mathbf {h} }$ (divide element-by-element)

• Note: While this step is useful for various applications as it normalizes the data set with respect to its variance, it is not integral part of PCA/KLT!

Project the z-scores of the data onto the new basis

• The projected vectors are the columns of the matrix

$\mathbf {Y} =\mathbf {W} ^{*}\cdot \mathbf {Z} =\mathbb {KLT} \{\mathbf {X} \}.$

• The columns of matrix Y represent the Karhunen–Loeve transforms (KLT) of the data vectors in the columns of matrix X.

Derivation of PCA using the covariance method

Let X be a d-dimensional random vector expressed as column vector. Without loss of generality, assume X has zero mean. We want to find a $d\times d$ orthonormal transformation matrix P such that

$\mathbf {Y} =\mathbf {P} ^{\top }\mathbf {X}$

with the constraint that

$\operatorname {cov} (\mathbf {Y} )$ is a diagonal matrix and $\mathbf {P} ^{-1}=\mathbf {P} ^{\top }.$

By substitution, and matrix algebra, we obtain:

${\begin{aligned}\operatorname {cov} (\mathbf {Y} )&=\mathbb {E} [\mathbf {Y} \mathbf {Y} ^{\top }]\\\ &=\mathbb {E} [(\mathbf {P} ^{\top }\mathbf {X} )(\mathbf {P} ^{\top }\mathbf {X} )^{\top }]\\\ &=\mathbb {E} [(\mathbf {P} ^{\top }\mathbf {X} )(\mathbf {X} ^{\top }\mathbf {P} )]\\\ &=\mathbf {P} ^{\top }\mathbb {E} [\mathbf {X} \mathbf {X} ^{\top }]\mathbf {P} \\\ &=\mathbf {P} ^{\top }\operatorname {cov} (\mathbf {X} )\mathbf {P} \end{aligned}}$

We now have:

${\begin{aligned}\mathbf {P} \operatorname {cov} (\mathbf {Y} )&=\mathbf {P} \mathbf {P} ^{\top }\operatorname {cov} (\mathbf {X} )\mathbf {P} \\\ &=\operatorname {cov} (\mathbf {X} )\mathbf {P} \\\end{aligned}}$

Rewrite P as d $d\times 1$ column vectors, so

$\mathbf {P} =[P_{1},P_{2},\ldots ,P_{d}]$

and $\operatorname {cov} (\mathbf {Y} )$ as:

${\begin{bmatrix}\lambda _{1}&\cdots &0\\\vdots &\ddots &\vdots \\0&\cdots &\lambda _{d}\end{bmatrix}}.$

Substituting into equation above, we obtain:

$[\lambda _{1}P_{1},\lambda _{2}P_{2},\ldots ,\lambda _{d}P_{d}]=[\operatorname {cov} (\mathbf {X} )P_{1},\operatorname {cov} (\mathbf {X} )P_{2},\ldots ,\operatorname {cov} (\mathbf {X} )P_{d}].$

Notice that in $\lambda _{i}P_{i}=\operatorname {cov} (\mathbf {X} )P_{i}$, P_i is an eigenvector of the covariance matrix of X. Therefore, by finding the eigenvectors of the covariance matrix of X, we find a projection matrix P that satisfies the original constraints.

Computing principal components with expectation maximization

In practical implementations especially with high dimensional data, the covariance method is rarely used because it is not efficient. Expectation maximization is one way to compute principal components efficiently. The following pseudo-code computes the first principal component of a data matrix, X^T, with zero mean, without ever computing its covariance:,

$\mathbf {p} =$ a random vector
do c times:
      $\mathbf {t} =0$
      for each row $\mathbf {x} \in \mathbf {X^{T}}$
            $\mathbf {t} =\mathbf {t} +(\mathbf {x} \cdot \mathbf {p} )\mathbf {x}$
      $\mathbf {p} ={\frac {\mathbf {t} }{|\mathbf {t} |}}$
return $\mathbf {p}$

The algorithm is nothing but the power iteration applied to the covariance matrix expressed as a sum of outer products(thereby avoiding direct calculation of the matrix itself, and thus requiring less computation). p will typically converge to the first principal component of X^T within a small number of iterations, c. (The magnitude of t will be larger after each iteration. Convergence can be detected when it increases by an amount too small for the precision of the machine.)

Subsequent principal components can be computed by subtracting component p from X^T (see Gram–Schmidt) and then repeating this algorithm to find the next principal component. However this simple approach is not numerically stable if more than a small number of principal components are required, because imprecisions in the calculations will additively affect the estimates of subsequent principal components. More advanced methods build on this basic idea, as with the closely related Lanczos algorithm.

One way to compute the eigenvalue that corresponds with each principal component is to measure the difference in sum-squared-distance between the rows and the mean, before and after subtracting out the principal component. The eigenvalue that corresponds with the component that was removed is equal to this difference divided by the number of dimensions (columns).

The NIPALS method

Main article: Non-linear iterative partial least squares

For very high-dimensional datasets, such as those generated in the 'omics sciences (e.g., genomics, metabolomics) it is usually only necessary to compute the first few PCs. The non-linear iterative partial least squares (NIPALS) algorithm calculates t₁ and p₁' from X. The outer product, t₁p₁' can then be subtracted from X leaving the residual matrix E₁. This can be then used to calculate subsequent PCs. This results in a dramatic reduction in computational time since calculation of the covariance matrix is avoided.

Relation between PCA and K-means clustering

It has been shown recently (2007) that the relaxed solution of K-means clustering, specified by the cluster indicators, is given by the PCA principal components, and the PCA subspace spanned by the principal directions is identical to the cluster centroid subspace specified by the between-class scatter matrix. Thus PCA automatically projects to the subspace where the global solution of K-means clustering lie, and thus facilitate K-means clustering to find near-optimal solutions.

Correspondence analysis

Correspondence analysis (CA) was developed by Jean-Paul Benzécri and is conceptually similar to PCA, but scales the data (which should be non-negative) so that rows and columns are treated equivalently. It is traditionally applied to contingency tables. CA decomposes the chi-square statistic associated to this table into orthogonal factors. Because CA is a descriptive technique, it can be applied to tables for which the chi-square statistic is appropriate or not. Several variants of CA are available including detrended correspondence analysis and canonical correspondence analysis. One special extension is Multiple correspondence analysis, which may be seen as the counterpart of principal component analysis for categorical data..

Generalizations

Nonlinear generalizations

Most of the modern methods for nonlinear dimensionality reduction find their theoretical and algorithmic roots in PCA or K-means. Pearson's original idea was to take a straight line (or plane) which will be "the best fit" to a set of data points. Principal curves and manifolds give the natural geometric framework for PCA generalization and extend the geometric interpretation of PCA by explicitly constructing an embedded manifold for data approximation, and by encoding using standard geometric projection onto the manifold. See also principal geodesic analysis.

Higher order

N-way principal component analysis may be performed with models such as Tucker decomposition, PARAFAC, multiple factor analysis, co-inertia analysis, STATIS, and DISTATIS.

Software/source code

• "ViSta: The Visual Statistics System" a free software that provides principal components analysis, simple and multiple correspondence analysis.
• "Spectramap" is software to create a biplot using principal components analysis, correspondence analysis or spectral map analysis.
• The Unscrambler is a multivariate analysis software enabling Principal Component Analysis (PCA) with PCA Projection.
• Computer Vision Library
• Multivariate Data Analysis Software
• In the MATLAB Statistics Toolbox, the functions princomp and wmspca give the principal components, while the function pcares gives the residuals and reconstructed matrix for a low-rank PCA approximation. Here is a link to a MATLAB implementation of PCA PcaPress .
• NMath, a numerical library containing PCA for the .NET Framework.
• in Octave, the free software equivalent to MATLAB, the function princomp gives the principal component
• in the open source statistical package R, the functions princomp and prcomp can be used for principal component analysis; prcomp uses singular value decomposition which generally gives better numerical accuracy. Recently there has been an explosion in implementations of principal component analysis in various R packages, generally in packages for specific purposes. There is no meaning in giving a complete list here, see [1].
• In XLMiner, the Principles Component tab can be used for principal component analysis.
• In IDL, the principal components can be calculated using the function pcomp.
• Weka computes principal components (javadoc).
• Software for analyzing multivariate data with instant response using PCA

See also

• Sparse PCA
• Biplot
• Eigenface
• Exploratory factor analysis (Wikiversity)
• Factor analysis
• Geometric data analysis
• Factorial code
• Independent component analysis
• Time Adaptive Self-Organizing Map
• Kernel PCA
• Matrix decomposition
• Nonlinear dimensionality reduction
• Oja's rule
• PCA network
• PCA applied to yield curves
• Point distribution model (PCA applied to morphometry and computer vision)
• Principal component regression
• Principal component analysis (Wikibooks)
• Singular spectrum analysis
• Singular value decomposition
• Transform coding
• Weighted least squares
• Dynamic mode decomposition

References

• R. Kramer, Chemometric Techniques for Quantitative Analysis, (1998) Marcel–Dekker, ISBN 0-8247-0198-4.
• Shaw PJA, Multivariate statistics for the Environmental Sciences, (2003) Hodder-Arnold.
• Patra sk et al., J Photochemistry & Photobiology A:Chemistry, (1999) 122:23–31

External links

• The Most Representative Composite Rank Ordering of Multi-Attribute Objects by the Particle Swarm Optimization
• Sub-Optimality of Rank Ordering of Objects on the Basis of the Leading Principal Component Factor Scores
• Spectroscopy and PCA
• An introduction and review of recent developments of PCA
• An introductory explanation of PCA from StatSoft
• A Tutorial on Principal Component Analysis by Jonathon Shlens (PDF)
• Principal Component Analysis using Hebbian learning tutorial
• Presentation of Principal Component Analysis used in Biomedical Engineering
• Application to microarray and other biomedical data
• PCA in functional neuroimaging, free software
• Uncertainty estimation for PCA
• FactoMineR, an R package dedicated to exploratory multivariate analysis
• A web-site with presentations and open source software on exploratory multivariate data analysis
• EasyPCA, a very simple and small PCA program under the GPL license
• R tutorial on cluster and principal component analysis including example data
• Simple COV-based PCA using Eigen Template Library in C++ by Antonio Gulli

Non-technical introductions

• A layman's introduction to principal component analysis (video)
• Step by step tutorial from KVL (video)
• Step by step introduction from Umetrics