Blending Independent Components and
Principal Components Analysis
2.1 What does ICA aim to achieve?
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2.1 What does ICA aim
to achieve?
Independent component analysis (ICA) is a tool for
extracting useful information from a large amount of data. It seeks to identify
the driving forces that underlie a set of observed phenomena. The phenomena to
which ICA could be applied are very wide ranging, including mobile phone
signals, stock price returns, brain imaging or voice recognition. It belongs to
a class of blind source separation (BSS) methods for separating data
into underlying informational elements, where ‘blind’ here means that such
methods endeavour to separate data into source signals even if very little is
known about the nature of the source signals.
Generally speaking, ICA is applied in a situation where
there are several distinct ‘output’ signals able to be measured, and it is
reasonable to postulate that these outputs depend on combinations of distinct
underlying ‘input’ signals or factors. The aim is, as far as possible, to
estimate or characterise these underlying signals. For example, with voice
recognition, the aim might be to differentiate between different foreground
contributors to the overall sound pattern and, additionally, to filter out, as
far as possible, anything that appears to be background noise.
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