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Blind Signal Separation using Principal Components Analysis

Published byΠαρθενιά Μαυρογένης Modified over 6 years ago

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Presentation on theme: "Blind Signal Separation using Principal Components Analysis"— Presentation transcript:

1 Blind Signal Separation using Principal Components Analysis
Alok Ahuja

2 Problem Formulation

3 Motivation Methods based on Higher Order Statistics
Computational burden Require large amount of data PCA utilizes Second Order Statistics Alleviates the computational cost Both differ in underlying assumptions

4 Principal Components Analysis
Reduction of feature dimension of data space Redundant feature removal e.g. Linear combination of features Eigen Analysis : Expansion of data vector in terms of its Eigen vectors This application : Algorithm used to find ALL of the Eigen vectors

5 Adaptive Principal Components Extraction (APEX) Algorithm
Train the network one neuron at a time Feedback from each neuron to all neurons that follow it Neurons are assumed to be linear Weight updates based on modified Hebbian learning rules

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