Blind Signal Separation using Principal Components Analysis

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Presentation transcript:

Blind Signal Separation using Principal Components Analysis Alok Ahuja

Problem Formulation

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

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

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