Independent Component Analysis & Blind Source Separation

Ata Kaban

The University of Birmingham

Overview

• Today we learn about – The cocktail party problem -- called also ‘blind

source separation’ (BSS)– Independent Component Analysis (ICA) for solving

BSS– Other applications of ICA / BSS

• At an intuitive & introductory & practical level

A bit like…

in the sense of having to find quantities that are not observable directly

Signals, joint density

time

Am

plitu

de

S1(t

)

Am

plitu

de

S2(t

)

Signals Joint density

s

marginal densities

Original signals (hidden sources) s1(t), s2(t), s3(t), s4(t), t=1:T

The ICA model

s1 s2s3 s4

x1 x2 x3 x4

a11

a12a13

a14

xi(t) = ai1*s1(t) + ai2*s2(t) + ai3*s3(t) + ai4*s4(t)

Here, i=1:4.

In vector-matrix notation, and dropping index t, this is x = A * s

This is recorded by the microphones: a linear mixture of the sources

xi(t) = ai1*s1(t) + ai2*s2(t) + ai3*s3(t) + ai4*s4(t)

The coctail party problem

Called also Blind Source Separation (BSS) problem

Ill posed problem, unless assumptions are made!

The most common assumption is that source signals are statistically independent. This means that knowing the value of one of them does not give any information about the other.

The methods based on this assumption are called Independent Component Analysis methods. These are statistical techniques of decomposing a complex data set into independent parts.

It can be shown that under some reasonable conditions, if the ICA assumption holds, then the source signals can be recovered up to permutation and scaling.

Determine the source signals, given only the mixtures

Recovered signals

Some further considerations

• If we knew the mixing parameters aij then we would just need to solve a linear system of equations.

• We know neither aij nor si.

• ICA was initially developed to deal with problems closely related to the coctail party problem

• Later it became evident that ICA has many other applications too. E.g. from electrical recordings of brain activity from different locations of the scalp (EEG signals) recover underlying components of brain activity

Illustration of ICA with 2 signals

s1

s2

x1

x2

Tt

tsatsatx

tsatsatx

:1

)()()(

)()()(

2221212

2121111

Original s Mixed signals

a2

a1

a1

Illustration of ICA with 2 signals

x1

x2

Tt

tsatsatx

tsatsatx

:1

)()()(

)()()(

2221212

2121111

Step1: Sphering

Step2: Rotatation

Mixed signals

a2

a1

a1

Illustration of ICA with 2 signals

s1

s2

x1

x2

Tt

tsatsatx

tsatsatx

:1

)()()(

)()()(

2221212

2121111

Step1: Sphering

Step2: Rotatation

Original s Mixed signals

a2

a1

a1

Excluded case

There is one case when rotation doesn’t matter. This case cannot be solved by basic ICA.

…when both densities are Gaussian

Example of non-Gaussian density (-) vs.Gaussian (-.)

Seek non-Gaussian sources for two reasons:* identifiability* interestingness: Gaussians are not interesting since the superposition of independent sources tends to be Gaussian

Computing the pre-processing steps for ICA

0) Centring = make the signals centred in zero

xi xi - E[xi] 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[yyT] denotes covariance matrix

V=E[xxT]-1/2 // can be done using ‘sqrtm’ function in MatLab xVx // 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

Fixed Point Algorithm

Input: X

Random init of W

Iterate until convergence:

Output: W, S

1)(

)(

WWWW

SXW

XWS

T

T

T

g

T

t

Tt

TGObj1

)()()( IWWΛxWW

0ΛWXWXW

TTgObj

)(

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 (WTV)

There are several g(.) options, each will work best in special cases. See FastICA sw / tut for details.

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)

Image denoising

Wiener filtering

ICA filtering

Noisy image

Original image

Clustering

In multi-variate data search for the direction along of which the projection of the data is maximally non-Gaussian = has the most ‘structure’

Blind Separation of Information from Galaxy Spectra

0 50 100 150 200 250 300 350-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Decomposition using Physical Models

Decomposition using ICA

Summing Up

• Assumption that the data consists of unknown components– Individual signals in a mix– topics in a text corpus– basis-galaxies

• Trying to solve the inverse problem:– Observing the superposition only– Recover components– Components often give simpler, clearer view of the

data

Related resources

http://www.cis.hut.fi/projects/ica/cocktail/cocktail_en.cgiDemo and links to further info on ICA.

http://www.cis.hut.fi/projects/ica/fastica/code/dlcode.shtmlICA software in MatLab.

http://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf Comprehensive tutorial paper, slightly more technical.