PCA

* PCA projects observations on a plane chosen so as to minimize the errors on the mutual "distances" of observations caused by the projection.

The PCA is a mathematical function that is used to transform a number of correlated variables into a smaller number of uncorrelated variables. These uncorrelated variables are called Principal Components.

Linear PCA versus nonlinear Principal Manifolds[1] for visualization of breast cancer microarray data: a) Configuration of nodes and 2D Principal Surface in the 3D PCA linear manifold.

than expected (Nigel Short: Quest for the Crown, by Cathy Forbes), the world champion and his challenger decided to play outside FIDE's jurisdiction, under another organization created by Kasparov called the Professional Chess Association (PCA).

Anyway, Huizinga & van der Molen conducted a principal components analysis (PCA) on these measures, a data reduction technique that is useful in consolidating the variance present in several measures to relatively fewer measures.

PCA is a maximum-likelihood technique for linear regression in the presence of Gaussian noise on both inputs and outputs. In some cases, PCA corresponds to a Fourier transform, such as the DCT used in JPEG image compression.

A linear autoassociator trained with sum-squared error in effect performs principal component analysis (PCA), a well-known statistical technique. PCA extracts the subspace (directions) of highest variance from the data.

Another data transformation that comes up is principal component analysis, one site with PCA software is the Carnegie-Mellon Statistics Library, see the file pca.c. I have never tried it.

very good tutorial. NEED TUTORIAL ON PCA AND ICA
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Kasparov defended his title in 1995 against the Indian superstar Viswanathan Anand, before the PCA collapsed (Intel, one of the major backers, pulled out).

Real-life Data: Case studies include US Postal Service Data for semiunsupervised learning using the Laplacian RLS Algorithm, how PCA is applied to handwritten digital data, the analysis of natural images by using sparse-sensory coding and ICA, ...

See also: Variance, Regression, Clustering, Distribution, Principal Component Analysis