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Independent Components Analysis

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What is ICA? “Independent component analysis (ICA) is a method for finding underlying factors or components from multivariate (multi- dimensional) statistical data. What distinguishes ICA from other methods is that it looks for components that are both statistically independent, and nonGaussian.” A.Hyvarinen, A.Karhunen, E.Oja ‘Independent Component Analysis’

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ICA Blind Signal Separation (BSS) or Independent Component Analysis (ICA) is the identification & separation of mixtures of sources with little prior information. Applications include: –Audio Processing –Medical data –Finance –Array processing (beamforming) –Coding … and most applications where Factor Analysis and PCA is currently used. While PCA seeks directions that represents data best in a Σ|x 0 - x| 2 sense, ICA seeks such directions that are most independent from each other. Often used on Time Series separation of Multiple Targets

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ICA estimation principles by A.Hyvarinen, A.Karhunen, E.Oja ‘Independent Component Analysis’ Principle 1: “Nonlinear decorrelation. Find the matrix W so that for any i ≠ j, the components y i and y j are uncorrelated, and the transformed components g(y i ) and h(y j ) are uncorrelated, where g and h are some suitable nonlinear functions.” Principle 2: “Maximum nongaussianity”. Find the local maxima of nongaussianity of a linear combination y=Wx under the constraint that the variance of x is constant. Each local maximum gives one independent component.

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ICA mathematical approach from A.Hyvarinen, A.Karhunen, E.Oja ‘Independent Component Analysis’ “Given a set of observations of random variables x 1 (t), x 2 (t)…x n (t), where t is the time or sample index, assume that they are generated as a linear mixture of independent components: y=Wx, where W is some unknown matrix. Independent component analysis now consists of estimating both the matrix W and the y i (t), when we only observe the x i (t).”

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The simple “Cocktail Party” Problem Sources Observations s1s1 s2s2 x1x1 x2x2 Mixing matrix A x = As n sources, m=n observations

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Classical ICA (fast ICA) estimation ICA Observing signalsOriginal source signal V4

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Motivation Two Independent Sources Mixture at two Mics a IJ... Depend on the distances of the microphones from the speakers

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Motivation Get the Independent Signals out of the Mixture

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ICA Model (Noise Free) Use statistical “latent variables“ system Random variable s k instead of time signal x j = a j1 s 1 + a j2 s a jn s n, for all j x = As IC‘s s are latent variables & are unknown AND Mixing matrix A is also unknown Task: estimate A and s using only the observeable random vector x Lets assume that no. of IC‘s = no of observable mixtures and A is square and invertible So after estimating A, we can compute W=A -1 and hence s = Wx = A -1 x

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Illustration 2 IC‘s with distribution: Zero mean and variance equal to 1 Mixing matrix A is The edges of the parallelogram are in the direction of the cols of A So if we can Est joint pdf of x 1 & x 2 and then locating the edges, we can Est A.

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Restrictions s i are statistically independent –p(s 1,s 2 ) = p(s 1 )p(s 2 ) Nongaussian distributions –The joint density of unit variance s 1 & s 2 is symmetric. So it doesn‘t contain any information about the directions of the cols of the mixing matrix A. So A cann‘t be estimated. –If only one IC is gaussian, the estimation is still possible.

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Ambiguities Can‘t determine the variances (energies) of the IC‘s –Both s & A are unknowns, any scalar multiple in one of the sources can always be cancelled by dividing the corresponding col of A by it. –Fix magnitudes of IC‘s assuming unit variance: E{s i 2 } = 1 –Only ambiguity of sign remains Can‘t determine the order of the IC‘s –Terms can be freely changed, because both s and A are unknown. So we can call any IC as the first one.

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ICA Principal (Non-Gaussian is Independent) Key to estimating A is non-gaussianity The distribution of a sum of independent random variables tends toward a Gaussian distribution. (By CLT) f(s 1 ) f(s 2 ) f(x 1 ) = f(s 1 +s 2 ) Where w is one of the rows of matrix W. y is a linear combination of s i, with weights given by z i. Since sum of two indep r.v. is more gaussian than individual r.v., so z T s is more gaussian than either of s i. AND becomes least gaussian when its equal to one of s i. So we could take w as a vector which maximizes the non-gaussianity of w T x. Such a w would correspond to a z with only one non zero comp. So we get back the s i.

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Measures of Non-Gaussianity We need to have a quantitative measure of non-gaussianity for ICA Estimation. Kurtotis : gauss=0(sensitive to outliers) Entropy : gauss=largest Neg-entropy : gauss = 0(difficult to estimate) Approximations where v is a standard gaussian random variable and :

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Data Centering & Whitening Centering x = x‘ – E{x‘} –But this doesn‘t mean that ICA cannt estimate the mean, but it just simplifies the Alg. –IC‘s are also zero mean because of: E{s} = WE{x} –After ICA, add W.E{x‘} to zero mean IC‘s Whitening –We transform the x’s linearly so that the x ~ are white. Its done by EVD. x ~ = (ED -1/2 E T )x = ED -1/2 E T Ax = A ~ s where E{xx ~ } = EDE T So we have to Estimate Orthonormal Matrix A ~ –An orthonormal matrix has n(n-1)/2 degrees of freedom. So for large dim A we have to est only half as much parameters. This greatly simplifies ICA. Reducing dim of data (choosing dominant Eig) while doing whitening also help.

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Computing the pre-processing steps for ICA 0) Centring = make the signals centred in zero x i x i - E[x i ] for each i 1) Sphering = make the signals uncorrelated. I.e. apply a transform V to x such that Cov(Vx)=I // where Cov(y)=E[yy T ] denotes covariance matrix V=E[xx T ] -1/2 // can be done using ‘sqrtm’ function in MatLab x Vx // for all t (indexes t dropped here) // bold lowercase refers to column vector; bold upper to matrix Scope: to make the remaining computations simpler. It is known that independent variables must be uncorrelated – so this can be fulfilled before proceeding to the full ICA

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Fixed Point Algorithm Input: X Random init of W Iterate until convergence: Output: W, S where g(.) is derivative of G(.), W is the rotation transform sought Λ is Lagrange multiplier to enforce that W is an orthogonal transform i.e. a rotation Solve by fixed point iterations The effect of Λ is an orthogonal de-correlation Aapo Hyvarinen (97) Computing the rotation step This is based on an the maximisation of an objective function G(.) which contains an approximate non-Gaussianity measure. The overall transform then to take X back to S is (W T V) There are several g(.) options, each will work best in special cases. See FastICA sw / tut for details.

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Application domains of ICA Blind source separation (Bell&Sejnowski, Te won Lee, Girolami, Hyvarinen, etc.) Image denoising (Hyvarinen) Medical signal processing – fMRI, ECG, EEG (Mackeig) Modelling of the hippocampus and visual cortex (Lorincz, Hyvarinen) Feature extraction, face recognition (Marni Bartlett) Compression, redundancy reduction Watermarking (D Lowe) Clustering (Girolami, Kolenda) Time series analysis (Back, Valpola) Topic extraction (Kolenda, Bingham, Kaban) Scientific Data Mining (Kaban, etc)

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Image denoising Wiener filtering ICA filtering Noisy image Original image

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Noisy ICA Model x = As + n A... mxn mixing matrix s... n-dimensional vector of IC‘s n... m-dimensional random noise vector Same assumptions as for noise-free model, if we use measures of nongaussianity which are immune to gaussian noise. So gaussian moments are used as contrast functions. i.e. however, in pre-whitening the effect of noise must be taken in to account: x ~ = (E{xx T } - Σ) -1/2 x x ~ = Bs + n ~.

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22 Exercise (part 1, Updated Nov 10) How would you calculate efficiently the PCA of data where the dimensionality d is much larger than the number of vector observations n? Download the Wisconsin Data from the UC Irvine repository, extract PCAs from the data, test scatter plots of original data and after projecting onto the principal components, plot Eigen values

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Ex1. Part 2 to subject: Ex1 and last 1.Given a high dimensional data, is there a way to know if all possible projections of the data are Gaussian? Explain - What if there is some additive Gaussian noise?

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Ex1. (cont.) 2. Use Fast ICA (easily found in google) de/dlcode.html de/dlcode.html –Choose your favorite two songs –Create 3 mixture matrices and mix them –Apply fastica to de-mix

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Ex1 (cont.) Discuss the results –What happens when the mixing matrix is symmetric –Why did u get different results with different mixing matrices –Demonstrate that you got close to the original files –Try different nonlinearity of fastica, which one is best, can you see that from the data

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References Feature extraction (Images, Video) –http://hlab.phys.rug.nl/demos/ica/http://hlab.phys.rug.nl/demos/ica/ Aapo Hyvarinen: ICA (1999) –http://www.cis.hut.fi/aapo/papers/NCS99web/node11.htmlhttp://www.cis.hut.fi/aapo/papers/NCS99web/node11.html ICA demo step-by-step –http://www.cis.hut.fi/projects/ica/icademo/http://www.cis.hut.fi/projects/ica/icademo/ Lots of links –http://sound.media.mit.edu/~paris/ica.htmlhttp://sound.media.mit.edu/~paris/ica.html object-based audio capture demos –http://www.media.mit.edu/~westner/sepdemo.htmlhttp://www.media.mit.edu/~westner/sepdemo.html Demo for BBS with „CoBliSS“ (wav-files) –http://www.esp.ele.tue.nl/onderzoek/daniels/BSS.htmlhttp://www.esp.ele.tue.nl/onderzoek/daniels/BSS.html Tomas Zeman‘s page on BSS research –http://ica.fun-thom.misto.cz/page3.htmlhttp://ica.fun-thom.misto.cz/page3.html Virtual Laboratories in Probability and Statistics –http://www.math.uah.edu/stat/index.htmlhttp://www.math.uah.edu/stat/index.html

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