Prasad, P Shiva (2007) Independent Component Analysis. BTech thesis.
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Abstract
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications,
including feature extraction and signal separation.
Item Type: | Thesis (BTech) |
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Uncontrolled Keywords: | Independent component analysis (ICA), Nongaussian |
Subjects: | Engineering and Technology > Electronics and Communication Engineering > Wireless Communications Engineering and Technology > Electronics and Communication Engineering > Intelligent Instrumentaion Engineering and Technology > Electronics and Communication Engineering > Soft Computing Engineering and Technology > Electronics and Communication Engineering > Signal Processing Engineering and Technology > Electronics and Communication Engineering > Artificial Neural Networks Engineering and Technology > Electronics and Communication Engineering > Data Transmission |
Divisions: | Engineering and Technology > Department of Electronics and Communication Engineering |
ID Code: | 52 |
Deposited By: | Anshul Baranwal |
Deposited On: | 05 May 2009 15:29 |
Last Modified: | 05 May 2009 15:29 |
Supervisor(s): | Panda, G |
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