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小 发表于 07-4-26 20:16 只看该作者
(eWiley) Independent Component Analysis-2001
(eWiley) Independent Component Analysis |  | 收藏 | | | 作者 | Aapo Hyv rinen and Erkki Oja, Helsinki University of Technology | | ＩＳＢＮ | 047140540X | | 出版日期/版本 | 2002 | | 格式 | ＰＤＦ Adobe Acrobat |
主题内容 | | 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. In the model, the data variables are
assumed to be linear or nonlinear mixtures of some unknown latent variables, and
the mixing system is also unknown. The latent variables are assumed nongaussian
and mutually independent, and they are called the independent components of the
observed data. These independent components, also called sources or factors, can be
found by ICA.
ICA can be seen as an extension to principal component analysis and factor
analysis. ICA is a much more powerful technique, however, capable of finding the
underlying factors or sources when these classic methods fail completely.
The data analyzed by ICA could originate from many different kinds of application
fields, including digital images and document databases, as well as economic
indicators and psychometric measurements. In many cases, the measurements are
given as a set of parallel signals or time series; the term blind source separation is used
to characterize this problem. Typical examples are mixtures of simultaneous speech
signals that have been picked up by several microphones, brain waves recorded by
multiple sensors, interfering radio signals arriving at a mobile phone, or parallel time
series obtained from some industrial process.
The technique of ICA is a relatively new invention. It was for the first time introduced
in early 1980s in the context of neural network modeling. In mid-1990s,
some highly successful new algorithms were introduced by several research groups,
together with impressive demonstrations on problems like the cocktail-party effect,
where the individual speech waveforms are found from their mixtures. ICA became
one of the exciting new topics, both in the field of neural networks, especially unsupervised
learning, and more generally in advanced statistics and signal processing.
Reported real-world applications of ICA on biomedical signal processing, audio signal
separation, telecommunications, fault diagnosis, feature extraction, financial time
series analysis, and data mining began to appear.
[ 本帖最后由 lemon508 于 07-4-26 20:50 编辑 ] |
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