Howtosignalprocessing
- What does independent component analysis do?
- What is ICA and PCA?
- How will you differentiate between PCA and ICA technique?
- Is Independent component analysis dimensionality reduction?
What does independent component analysis do?
Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples.
What is ICA and PCA?
Independent Component Analysis (ICA)
Principal Component Analysis (PCA) ICA optimizes higher-order statistics such as kurtosis. PCA optimizes the covariance matrix of the data which represents second-order statistics. ICA finds independent components. PCA finds uncorrelated components.
How will you differentiate between PCA and ICA technique?
While the goal in PCA is to find an orthogonal linear transformation that maximizes the variance of the variables, the goal of ICA is to find the linear transformation, which the basis vectors are statistically independent and non-Gaussian.
Is Independent component analysis dimensionality reduction?
ICA is a linear dimension reduction method, which transforms the dataset into columns of independent components. Blind Source Separation and the "cocktail party problem" are other names for it. ICA is an important tool in neuroimaging, fMRI, and EEG analysis that helps in separating normal signals from abnormal ones.
