Technical GlossaryMachine Learning

Independent Component Analysis

A dimensionality reduction and separation method that aims to decompose mixed signals into statistically independent components.

Independent Component Analysis is especially useful for separating mixed signals and identifying hidden independent sources. Examples include EEG signals, audio mixtures, and some financial time-series settings. Unlike PCA, it does not focus only on variance, but on statistical independence. This makes it a powerful alternative for signal separation and latent structure discovery.

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Dimensionality Reduction

An approach that reduces the number of variables representing data while preserving as much useful information as possible.

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Singular Value Decomposition (SVD)

A powerful decomposition method that breaks a matrix into more fundamental components for structure analysis, compression, and dimensionality reduction.

Linear Regression

A fundamental regression algorithm that models the linear relationship between input variables and a target variable.

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Independent Component Analysis

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Dimensionality Reduction

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