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Blind Signal Separation using Principal Components Analysis

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

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Blind Signal Separation using Principal Components Analysis

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  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 findALLof 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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