Questions tagged [ica]
Independent Component Analysis separates the additive combination of multiple signals into their estimated components.
68
questions
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1answer
33 views
Why ICA doesn't work on Gaussian data
Examples I found explain this using standard Gaussian data, i.e. $\mathcal{N}(0, I)$ (e.g. in Andrew Ng CS229 lecture page 3), saying that if so, mixing matrix with arbitrary rotations can not be ...
2
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2answers
44 views
independence in independent component analysis
ICA is quite popular for analyzing brain images (e.g. group ICA). One common assumption/constraint is that the signals in the brain come from "independent spatial sources".
I'm confused ...
3
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0answers
29 views
ICA: a question about the non-gaussian requirement
I'm new in the ICA processing and I'm trying to understand the non-gaussian requirement. I read that the problem is that, if the composed data is $\mathbf{x}=\mathbf{As}$ with $\mathbf{A}$ (unknown) ...
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0answers
21 views
What is the difference in the “solutions” of FastICA and Infomax?
So, I feel like I can understand the basic difference between FastICA and Infomax:
Infomax tries to minimize the mutual information between variable.
FastICA tries to maximize the non-gaussianity of ...
0
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0answers
11 views
Blind source separation using FastICA
Reading the example by scikit-learn on how to use the FastICA function, I couldn't understand the following plot:
In the third plot ("ICA recovered signal") - why is the magnitude different ...
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0answers
11 views
Independent Component Analysis (ICA): fewer sources than features (fastICA)
I am trying to understand how the ICA by A. Hyvärinen, J. Karhunen, E. Oja (2001) works in practice. In particular, I have problems trying to understand its application to factor-analysis-kind of ...
1
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0answers
15 views
Relationship between minimizing mutual information and maximizing non-Gaussianity in Independent Component Analysis (ICA)
In independent component analysis (ICA), we assume that the observe data $\bf{x}$ from $n$ channels come from linear mixing of $n$ independent sources $\bf{s}$:
$$
\bf{x}=\bf{As}
$$
And we try to find ...
2
votes
1answer
38 views
Proof that the mutual information I(X;Y) between two random variables X and Y is 0 if and only if X and Y are independent
On the Wikipedia of mutual information it says that $I(X;Y)=0$ if and only if $X$ and $Y$ are independent.
I can proof that if $X$ and $Y$ are independent, then $I(X;Y)=0$, because $p(x,y)=p(x)p(y)$. ...
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0answers
34 views
Conduct ICA on a subset of the dataset and using rest of the data to extract timeseries for prediction (CV-classifier)?
I have an issue regarding cross-validation and dimensionality reduction, specifically in the realms of neuroimaging.
What I found out so far is that:
if I wanted to conduct ICA on a dataset in order ...
0
votes
0answers
36 views
GO-GARCH using Fast-ICA rmgarch package with MVNORM distribution
R package rmgarch by Ghalanos estimates the GO-GARCH model using fast-ICA.
I have read that ICA aims to separate a multivariate signal into additive sub-...
0
votes
0answers
16 views
ICA for Noise Reduction over a Dataset
Suppose my dataset consists of $N$ example vectors $\mathbf{x}_{1}, \ldots, \mathbf{x}_{N}$ where $\mathbf{x}_{n} \in \mathbb{R}^{p}$ $\forall n$. I assume that each vector $\mathbf{x}_{n}$ is ...
0
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0answers
34 views
Why signals can be assumed non-Gaussian in ICA?
I understand why assumption of non-gaussianity is needed in ICA-model. I just can't find any source for why some signals e.g sound signals can be assumed to be non-gaussian. Doesn't everything in ...
0
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0answers
16 views
whiteness vs Uncorrelatedness
While studying ICA in the book by Aapo Hyvarinen I found the following scentence:
"A slightly stronger property than uncorrelatedness is whiteness. Whiteness of a
zero-mean random vector, say y, ...
0
votes
1answer
95 views
ICA and orthogonality of Independent Components
In the book by Aapo Hyvärinen, it is shown that:
Where z is the white vector of a data matrix x, s are the IC's and à is the mixing matrix of the whitened data matrix z. My question is: If the matrix ...
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0answers
30 views
Can I combine independent components from different models using PCA?
I have a set of independent components for each subject in my dataset (i.e. an ica model was generated for each subject). The samples used to generate each set of ICs are aligned across subjects, and ...
1
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1answer
126 views
FastICA results not exactly consistent on repetition
I have asked this on stack overflow but couldn't get an answer. I am using the fastICA implementation in R. Example code:
...
0
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0answers
182 views
Estimation of NegEntropy
I am trying to evaluate the different ICA algorithms. To do that, one of the measure which I use, is to estimate the non-gaussianity using NegEntropy. I am trying to find a formula/function which can ...
0
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1answer
57 views
Principal component analysis in two dimensions
During my studies, I stumbled upon the following exercise:
We have the following joint probability distribution:
$$p(x,y) = p(x) p(y|x)$$
$$p(x) = \mathcal{N}(0,1), p(y \mid x) = \frac{1}{2} \delta(y ...
1
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0answers
11 views
Blind source seperation on space data [closed]
in a uni project we gathered spectrum data from the ISS (time x frequency).
The challenge is now to analyse this data and especially try to seperate the signals.
As far as I understood it the common ...
0
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0answers
39 views
Making vectors independent of each other
Assume that I have three vectors $A, B, C$ containing information about a set of variables. There may be common information shared between these vectors, that is $A, B, C$ are not independent.
What ...
1
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1answer
53 views
Testing statistical independence before using ICA
First a little bit of background.
I'm interested in exploring the performance of Independent Component Analysis (ICA) in the context of disentangling intracranial EEG signals. These signals are ...
1
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1answer
615 views
What does ICA return?
I am confused with ICA. With PCA I understood that it always gives the components with maximum variance. What does ICA return? Does it return components with maximum independence? How to find best ...
1
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1answer
31 views
FastICA of orthonormal inputs
In Wikipedia page of FastICA it says
FastICA seeks an orthogonal rotation of prewhitened data
Which means if the input is orthonormal, so would be the output. This can be examined numerically but ...
2
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0answers
134 views
ICA for noise reduction of covariance matrix
Trying to understand ICA in the context of noise reduction of covariance matrices (of dimensionality M).
I understand in PCA, you can reconstruct the covariance matrix by squaring the first N ...
2
votes
1answer
424 views
Doesn't the non-Gaussian source assumption of ICA render it practically useless?
Gaussian distributions appear everywhere in nature, indeed this was largely the justification for most classical methods' reliance on assumption of normality.
ICA assumes non-Gaussian sources, indeed ...
1
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2answers
64 views
The lack of correlation determines the second-degree cross-moments (covariances) of a multivariate distribution?
It is given in the following image that
lack of correlation determines the second-degree cross-moments
(covariances) of a multivariate distribution,while in general statistical independence ...
1
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0answers
650 views
Why does the Independent Component Analysis require non-gaussian?
This I found on google while I was going through the Independent Component Analysis in unsupervised learning.
Let x = As where A is the Mixing Matrix.
So, Lets assume that s here is Gaussian ...
7
votes
1answer
1k views
What is mean by the non-gaussianity in the independent component analysis(ICA)?
What is mean by non-gaussianity in ICA? Why do we use in ICA? How is Non-Gaussianity is an important and essential principle in ICA estimation?
Following is the statement I found in a research paper....
2
votes
1answer
971 views
Numerical Example of Independent Component Analysis
Can somebody explain ICA(Independently Component Analysis) with a small practical example over here.
I have seen lot of programs and libraries written and you can just apply that to your data to find ...
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0answers
49 views
Connecting PCA singular values to generating parameters
I have a non-linear function of the following form
$y_i = f(x_i,\theta)$
where $x_i$ is known and $\theta$ is a vector of parameters. I have generated $n$ realizations of $y_i$ by sampling from a ...
9
votes
0answers
534 views
Why are Gaussian distributions the only “forbidden” source distribution for ICA?
I know it's commonly asked why Gaussians are forbidden from use in independent components analysis. This is because a gaussian source distribution will result in the same observed distribution no ...
1
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1answer
67 views
Why is infinite data required to verify statistical independence
I've been reading about Independent Component Analysis and the FastICA algorithm. The wiki page for FastICA states:
FastICA is an efficient and popular algorithm for independent
component ...
1
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0answers
51 views
Understanding Natural gradient learning Independent component analysis
I am quite a novice to statistics and am currently fighting my way through the "Neural Networks and Machine Learning" Book by Haykin. (p.516-518)
In the discussion about independent component analysis ...
2
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2answers
604 views
ICA, how to check for Gaussian components?
Independent Component Analysis (ICA) requires that at most one of the additive subcomponents of a multivariate signal is Gaussian. If I do not know the distributions of the subcomponents, how do I ...
9
votes
1answer
1k views
PCA is to CCA as ICA is to?
PCA looks for factors in data that maximize explained variance. Canonical correlation analysis (CCA), as far as I understand, is like an PCA but looks for a factors that maximize cross covariance ...
0
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1answer
267 views
ICA - independence of coefficients and maximizing independence
Hopefully this isn't too silly a question but I'm wondering how in independent component analysis when we've got independent coefficients then we identify parts of a face such as eyes, mouth, nose, ...
1
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1answer
2k views
Using only one mic in Cocktail Party Algorithm
So I came across this piece of code that separates 2 audio sources from 2 mixed audio sources has shown here:
...
0
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2answers
286 views
Why are gradient-based methods better for nonstationary environments?
In the book Independent Component Analysis (Hyvärinen et al. 2001), it is mentioned on page 178:
The advantage of such gradient methods, closely connected to learning in neural networks, is that ...
0
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1answer
851 views
What is the advantage of FastICA over other ICA algorithms?
I have seen that FastICA is the only ICA algorithm implemented in many packages. What are the advantages of FastICA compared to other algorithms? What are its disadvantages?
2
votes
1answer
61 views
ICA - extract one non-Gaussian source among many Gaussian sources with a-priori information
I have $N$ mixtures consisting of one non-Gaussian source (I know the distribution) and many (more than $N$) Gaussian sources.
I also know how my non-Gaussian source is mixed into the signal (I know ...
4
votes
1answer
3k views
PCA vs FA vs ICA for dimensionality reduction in questionaire data
I am trying to identify personality traits underlying the multidimensional data from a questionnaire. In more abstract terms this means reducing the dimensionality of my data from N-dimensional (where ...
4
votes
1answer
603 views
What does the number of independent components produced by ICA depend on?
I'm a student working on my bachelor thesis performing
independent component analysis (ICA) on some fMRI data using MELODIC FSL.
I would like to ask some questions regarding the results of ICA. ...
1
vote
2answers
2k views
Project new data using independent components analysis
I’m trying to project new data into a space I created with icafast. The R package ica does not come with it's own predict function. Here is my attempt to do so. This is in vein of what I can do with ...
0
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1answer
103 views
Is it possible to apply PCA to the reconstructed data of ICA?
I have read that it is not possible to verify the variance and order of independent component extracted from ICA.
So, I reconstructed the spectra from matrix A and ST(independent component) then ...
3
votes
1answer
5k views
Why are we using ICA? [duplicate]
I am new to Independent Component Analysis (ICA) and have a rudimentary understanding of the method.
I understand that PCA finds vectors on which the projected data has maximum variance. ICA, on ...
2
votes
1answer
6k views
The Independence in Independent Component Analysis - Intuitive Explanation
I could use a little bit of help in understanding a concept with regards to ICA:
ICA decomposes a multivariate signal into 'independent' components through 1. orthogonal rotation and 2. maximizing ...
3
votes
3answers
2k views
Evaluate output of different dimensionality reduction methods
I used PCA, ICA, and FA to perform dimensionality reduction on my data. How can I measure which method performed best? If I reduce my data to 3 dimensions and plot it, what type of trends would ...
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2answers
4k views
Making sense of independent component analysis
I have seen and enjoyed the question Making sense of principal component analysis, and now I have the same question for independent component analysis. I mean I want to make a comprehensive question ...
2
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1answer
39 views
Identifiability of a particular Independent Component Analysis model
I am considering the model :
$$ \mathbf{x} = \mathbf{A}\mathbf{s} $$
where $\mathbf A \in \mathcal{M}_{n,p}(\mathbb{R})$ and $\mathbf s \in \mathbb{R}^{p}$ such that the entries of $\mathbf s$ are i....
1
vote
1answer
209 views
ICA: plotting independent components
How do I find the directions of the independent components if I have already found the mixing matrix?
Let's say that I have this mixing matrix:
$$\mathcal W = \begin{bmatrix} 2 & -2 \\ 2 & 4 ...
