Independent Component Analysis

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)
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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